Daniela Rincón

Collective Intelligence for Decision-Making in Complex Environments: Literature Review

Keywords: Collective Intelligence, Swarm Intelligence, Complex Systems, Human Social Systems. S4

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Human Social Systems must develop ways that make possible to increase the agility to adapt to changing conditions in the environment, considering its increasing complexity. Adaptation is understood from the biological -evolutionary perspective, and involves variations that enable the system to provide solutions to survive in the environment [START_REF] Bock | The Definition and Recognition of Biological Adaptation[END_REF].

Collective intelligence (CI), studied in Natural Social Systems (NSSs), in Artificial Social Systems (ASSs) and in Human Social Systems (HSSs), is an option to increase agility in adapting to changing environmental conditions. Research in NSSs has its origin in Swarm Intelligence (SI); term that was introduced for the first time by Beni and Wang [START_REF] Beni | Swarm Intelligence in Cellular Robotic Systems[END_REF], when they investigated the emergence of collective behaviors. This concept was later extended by Bonabeau, Dorigo and Theraluz [START_REF] Boneabeau | Swarm Intelligence: From Natural to Artificial Systems 4[END_REF] including any effort to design algorithms for problem solving inspired by the collective behavior of social insects.

In human social systems, CI was formalized since the 1990s, by authors such as Lévy [4], Pór [START_REF] Pór | The quest for collective intelligence[END_REF] and Malone [START_REF] Malone | What is collective intelligence and what will we do about it? Collect[END_REF]; and it is understood from the behaviors and decisions that arise from the crowds. CI appears as a mean to take advantage of the large volumes of information, which arise permanently on account of technological development, in the generation of new knowledge that acts as a society engine. [4] The different proposals of CI in HSSs have been oriented to the design of systems with capacities to solve problems in flexible, robust, adaptive, agile and resilient ways, allowing them to survive in highly changing environments [START_REF] Alberts | Power to the edge: Command[END_REF]. However, a consensus regarding what are the structural and dynamic aspects that facilitate their understanding and emergence does not exist thus far.

Methodology

The literature available in CI, mainly in NSSs and ASSs, is extensive and has a broad trajectory. The study was carried out in three stages: S1 embraces wide database searches. Based on the results of S1, an emphasis in certain authors who are identified as relevant (Table 1) is given on S2, and finally, S3 focuses on the analysis of the obtained information.

S3

Comparison of the NSSs and ASSs structural and dynamic factors with those of HSSs identified in S2, in order to recognize differences and similarities.

Collective Intelligence in Human Systems

High levels of robustness, flexibility, adaptability, cooperation, Self-Organization and Collective Intelligence for environmental solution problems that NSSs and ASSs exhibit, has aroused the interest of various authors to design HSSs models. [START_REF] Atlee | Por G Collective Intelligence as a Field of Multidisciplinary Study and Practice[END_REF]. The research conducted by McHugh [17] evidences that organizations with higher levels of CI present higher quality of decisions in terms of precision, agility [START_REF] Gilliland | Quality and quantity goals in a complex decision task: Strategies and outcomes[END_REF] variety of knowledge [START_REF] Bantel | Top management and innovations in banking: Does the composition of the top team make a difference?[END_REF], breadth and depth [START_REF] Kraiger | A framework for understanding and measuring shared mental models of team performance and team effectiveness[END_REF]. As mentioned by Alberts [21], even when an absence of agility does not represent a threat to existence, it is very expensive.

To date there are important investigations for CI in HSSs from different disciplines such as psychology [22], politics [START_REF] Gilliland | Quality and quantity goals in a complex decision task: Strategies and outcomes[END_REF] and economics [START_REF] Guo | Multiagent-based scheduling optimization for Intelligent Manufacturing System[END_REF]. Table 3 presents some of the CI proposals in HSSs. The registered proposals cover different sectors (health, education, energy, among others) and are mainly oriented towards decision making. Proposals registered in Table 3 provide the framework that enables the identification of structural and dynamic factors that explain the CI in the studied systems. In Table 3, each model is identified by an ID number that will be used to reference the model in some sections of the document.

Gap between Natural and Artificial Systems Compared to Human Systems

The aim of this section is to identify the structural and dynamic features that facilitate the understanding of CI in NSSs and ASSs to enrich CI proposals in HSSs. In this research it is recognized that NSSs and ASSs do not have a specific and predetermined purpose as it happens in HSSs e.g. profitability. The pre-specification of the system's purpose can reduce the complexity this can absorb. In order to know how the HSSs could enhance their CI characteristics and improve decision-making in environments with high levels of uncertainty, it is necessary to contrast features they have in common with the NSSs and the ASSs. The structural and dynamic features identified in NSSs and ASSs, HSSs and the gap between Natural and Artificial Systems Compared to Human Systems are shown below (Table 4).

Table 4. Structural and dynamic features in NSSs and ASSs

NSSs and ASSs

HSSs Gap

Structural Features

Central Controller absence. The centrality is a structural attribute of the network nodes and not of the actors themselves, i.e. it is understood from the position occupied by an agent in the system [START_REF] Hossain | Communications network centrality correlates to organisational coordination[END_REF]. Control is oriented to guarantee the fulfillment of the objectives or the development of tasks [START_REF] Simons | Levers of control: How managers use innovative control systems to drive strategic renewal[END_REF]. Diverse investigations have shown that the CI exhibited by social insects is characterized by the

Autonomy.

It is described as multidisciplinary groups with selforganizing that operate with a decentralized authority [START_REF] Ismail | Organizaciones Exponenciales[END_REF]. For a collective to be intelligent, the autonomous perceptions of those who make up the group must be mobilized and respected: call for the active expression of singularities, creativities and competences.

[34] In models 1, 2, 3 and 4 there is not a central node that makes decisions and exercises control over others, but, a decision is made according to a series of pre-established rules.

Decentralization

and Autonomy. While within NSSs and ASSs exist a full SO in the absence of a central controller to provide an agile solution to the group and environment needs, in HSSs there is not a complete decentralization. In NSSs, the division of labor is usually determined by morphological features.

[3] From the analyzed models that use digital tools to fulfill their purpose (1, 2, 4, 6, and 8), platforms are means for absence of a central controller and a hierarchical structure. [START_REF] Greene | Interaction rate informs harvester ant task decisions[END_REF].

cooperation between individuals with different roles and achieve a common goal, therefore, a central controller does not exist.

Direct/Indirect Communication, Information

Processing, Shared Knowledge and Collaboration. Communication in NSSs, ASSs and HSSs seeks to process information in an effective and agile way among the system agents. Nevertheless, agility in HSSs interactions cannot be considered as effective as in the NSSs. Authors such as Dale, Fusaroli, Duran and Richardson [START_REF] Dale | The Self-Organization of human interaction[END_REF] propose the study of synchrony patterns between individuals. On the other hand, Barlow and Dennis [START_REF] Barlow | Not As Smart As We Think: A Study of Collective Intelligence in Virtual Groups[END_REF] raise the importance of the medium in which the homogeneity in the signals interpretations is carried out to achieve coherent responses.

Robustness.

In NSSs and ASSs it is observed that even though one of its components fails, the system has the ability to continue functioning as usual. In the models of HSSs using virtual media, despite identifying certain robustness, the characteristics of the human being (beliefs, thoughts, and feelings) can influence the collective performance. Models 3 and 5 may be affected by the amount of people who make them up.

Self-Organization. SO describes the emerging responses to different situations that the environment may present. In HSSs information can condition the collective conduct. This idea can be identified in model 7, where, according to the quality information held by an agent, it could make decisions faster or slower according to the role. Models 1, 2 and 4 display how from the agents' opinions in real Direct Interaction. Some forms of direct communication in social insects are antennae, trophalaxis (feeding one another), mandibular contact, eye contact, chemical contact, among others [START_REF] Boneabeau | Swarm Intelligence: From Natural to Artificial Systems 4[END_REF]. Another example is birds attracted to sites where other birds already nest. This imitative behavior is a positive feedback process in which an individual follows the "I nest near where you nest" rule. [START_REF] Camazine | Self-organization in biological systems[END_REF].

Direct communication. Gestures, language, context and prosody (pronunciation and accentuation) influence the interaction between agents. [START_REF] Dale | The Self-Organization of human interaction[END_REF]. Direct communication characterizes model 6 as soon as group decisions between agents were made face to face in the university facilities.

Indirect interaction. Refers to communication processes that arise through the modification of the environment. When termites build their nests, they send stimuli to others through pheromones and it causes other termites to build their nests close to the one previously placed [START_REF] Grassé | The automatic regulations of collective behavior of social insect and " stigmergy[END_REF]. Indirect interaction also called stigmergy by the French biologist Grassé [START_REF] Grassé | The automatic regulations of collective behavior of social insect and " stigmergy[END_REF] allows to understand the cooperative behavior that arises among insects.

Indirect communication.

Agents do not require to perceive the existence of others. However, the activities that are carried out in the system have consequences for all its actors [22]. In model 1, 2, 4, 8 the information exchange is generated through non-explicit assumptions in virtual media, without a face-to-face contact.

Division of roles.

It allows creating specialized groups that perform tasks better than if they were performed by a single agent [START_REF] Park | Measures to take advantage of the collective collective intelligence of the Web 2.0 platform[END_REF], this ensure that if a member fails in the development of the labor, colony is still functioning through the work of other agents.

Distributed

Information Processing. Information is not concentrated on specific groups but rather integrates naturally to the entire human activities and returns to the hands of all. Collectives communicate transversally without a hierarchical structure.

[34] In model 4 ideas are produced individually-collectively and are hierarchized; then, they are assessed.

Dynamic Features

Self-organization (SO). It Is understood as the emergence of collective behaviors from interactions between agents [START_REF] Kauffman | The illusion of full inclusion: A comprehensive critique of a current special education bandwagon[END_REF] or as a set of dynamic mechanisms in which global-level structures appear from an interactions system among

Self-Organization.

Behavior patterns emerges from interaction. These patterns are formed and reformed spontaneously and continuously at multiple levels within the system. The objective is to understand how they are determined by the interactions of the agents to carry out the appropriate interventions in the system. [START_REF] Eoyang | Conditions for Self-Organizing in Human Systems[END_REF]. In models 1, 2 and 4 when an individual participates the components of different levels [START_REF] Bonabeau | Selforganization in social insects[END_REF]. and contributes new ideas, the other agents are stimulated and actively participate.

time some system conditions can be changed.

Adaptability and Flexibility.

Different species and biological systems look for adapt agilely to changing and emerging environments. Some approaches have been made in the proposals that use virtual media as mechanisms to achieve the proposed intentions. In models that perform medical diagnoses using CI [START_REF] Boneabeau | Swarm Intelligence: From Natural to Artificial Systems 4[END_REF][START_REF] Pór | The quest for collective intelligence[END_REF] Adaptability. SO allows to explain adaptability as the capacity of modify and being modified as well as adjust itself to the changing environment.

Adaptability. If a link is congested, the interconnection between agents must allow an alternative path to be used to reach its destination, whether it is information or a decision. [START_REF] Peters | Analytical and numerical investigation of ant behavior under crowded conditions[END_REF]. In the model 4, the students feedback towards the proposed ideas changes according to the other students opinions.

Flexibility. Denotes the capacity of changing within the system.

Collaboration. The capacity of a multiagent system is not determined by a simple agent but by the intelligence shown by mutual coordination from the interactions that are generated in the system. [START_REF] Guo | Multiagent-based scheduling optimization for Intelligent Manufacturing System[END_REF] Information and Communication Technologies (ICTs) are used in model 8 as facilitators in the collaborative environments creation to gather resources and experts to relate existing knowledge.

Robustness. Refers to the ability to continue operating despite failures that occur at individual level [START_REF] Boneabeau | Swarm Intelligence: From Natural to Artificial Systems 4[END_REF] Shared knowledge. One person can represent a source of knowledge enrichment for another one. By associating their competences, they demonstrate that they can do a better job together than apart. [22,34]. The model 7 have two roles. Leaders have the most accurate information and decide quickly.

Discussions and Future Research Lines

The development of this article has allowed to understand and identify structural and dynamic features present in the NSSs, ASSs and HSSs. Within the features found in the HSSs models closest to the characterization in NSSs and in ASSs, are the absence of a central controller (NSSs) and autonomy (HSSs). Despite the similarity, the roles within the system are predefined. It is necessary that the function an actor plays within the system properly emerges from the interactions with the environment. On the other hand, proposals where virtual platforms are included, information processing presents a good approach to what is set in the NSSs; virtual media allows greater flexibility in the systems. However, quality of input information determines how agile the system can be and its capacity for self-organization; therefore, collaboration emerges as a possible response to avoid aspects such as opportunism and other characteristics of the human being that provide limited information and slow down the system.

A proposal for future research in the CI models design is to identify the range in which the number of participating actors should be found to decrease the influence of specific nodes, and ensure that it does not decrease the level of interactions between agents and the environment. The use of ICTs tools could improve the design of a CI model, creating information bases, supporting them on adaptive learning and leading the system to adapt itself to changes in the environment.

Table 1 .

 1 Main Databases, Journals and Authors consulted.

	Collective Intelligence review	Databases	Main Journals	Main Authors
		ScienceDirect	MIT Sloan Management Review	Bonabeau, E (1999)
		SpringerLink	Administrative Science Quarterly	Secundo G. (2012)
	Natural and Artificial Social Systems	Scopus JSTOR World Scientific	Philosophical Logic and Artificial Intelligence Artificial Intelligence	Malone, T.W. (2010) Lévy, P. (1997) Atlee, T. (2000)
		Elsevier	Advances in Complex Systems	Gerardo Beni (1993)
		IEEE	Integrative and Comparative Biology	Jing Wang (1993)
		ScienceDirect	MIT Sloan Management Review	Malone, T.W. (2008)
		SpringerLink	Cognitive Systems Research	Lévy, P. (2004)
	Human Social Systems	Scopus	Strategic management journal	Pór G (1995)
		APA PSYCNET	Advances in Complex Systems	McHugh K.A. (2016)
	The methodology proposed for the development of the present investigation was carried
	out in 4 stages indicated. (Table 2.)

Table 2 .

 2 Stages of the methodology. Prepared by authors.

	Stage	Definition
	S1	CI literature review in NSSs and ASSs. CI literature review in HSSs
	S2	Identification of the structural and dynamic features that different models applying CI in HSSs have in
		common. (Structural factors refer to features that the system structure should have. Dynamic factors focus on
		the features that emerge from the structure of the system

Table 3 .

 3 Models with CI Applications. Prepared by the Authors. Using the Collective Intelligence for inventive problem solving: A contribution for Open Computer Aided Innovation.[START_REF] Flores | Using the Collective Intelligence for inventive problem solving: A contribution for Open Computer Aided Innovation[END_REF]Flores, R. L., Belaud, J. P., Le Lann, J. M., & Negny, S. (2015). Collaboration, interaction and use of knowledge between a company and the market for solving inventive problems in the industrial sector.

		(2016).Teaching IC model using a virtual platform to promote shared learning between students and teachers
	5	How common standards can diminish collective intelligence: a computational study. [28]Morreau, M., & Lyon,
		A. (2016) Create knowledge shared by a group of specialists through the collection of information and individual
		experiences to improve the medical diagnoses efficiency

ID PROPOSAL

1

Collective Intelligence and Online Learning Communities

[START_REF] Gea | Collective Intelligence and Online Learning Communities[END_REF]

. Gea, M., Soldado, R. M., & Lenguajes, D. (2011). New forms of non-formal learning skills in digital communities through Collective Intelligence. 2 Collective intelligence for promoting changes in behavior: a case study on energy conservation [25].Piccolo, L. S. G., Liddo, A. De, Burel, G., Fernandez, M., & Alani, H. (2017) .Build knowledge in a collaborative manner around the conservation of energy. 3 Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist [26].Wolf, M., Krause, J., Carney, P. A., Bogart, A., & Kurvers, R. H. J. M. (2015).The proposal studies the way as certain established rules are used by a team of specialists in mammograms diagnosis and treatments. 4 Fostering collective intelligence education [27]Meza, J., Monguet, J. M., Grimón, F., & Trejo, A.

  low level of adaptability is evidenced.

	Agility. In model 2 and 8
	technology is used to create an
	agile model with the aim of
	generating shared awareness
	around the environmental care
	and open innovation through the
	improvement of communication.
	Apparently, these models allow
	solutions to different cases in less
	time and with better results.


Lucas Rizzo

Ljiljana Majnaric

Pierpaolo Dondio

Luca Longo
email: luca.longo@dit.ie

An investigation of argumentation theory for the prediction of survival in elderly using biomarkers

Keywords: Biomarkers, argumentation theory, defeasible reasoning

Research on the discovery, classification and validation of biological markers, or biomarkers, have grown extensively in the last decades. Newfound and correctly validated biomarkers have great potential as prognostic and diagnostic indicators, but present a complex relationship with pertinent endpoints such as survival or other diseases manifestations. This research proposes the use of computational argumentation theory as a starting point for the resolution of this problem for cases in which a large amount of data is unavailable. A knowledge-base containing 51 different biomarkers and their association with mortality risks in elderly was provided by a clinician. It was applied for the construction of several argument-based models capable of inferring survival or not. The prediction accuracy and sensitivity of these models were investigated, showing how these are in line with inductive classification using decision trees with limited data.

Introduction

In the medical domain, biomarkers can be objectively defined as a medical condition which can be measured precisely and reproducibly. It has to be observed from outside the patient, contrarily to medical symptoms which are an indication of health recognized by the patients themselves [START_REF] Strimbu | What are biomarkers?[END_REF]. Simple examples include blood pressure, pulse and waist circumference. It can also range to several diseases diagnostics or more complex laboratory tests of blood and other tissues. Over the past 50 years the advances in biological sciences have generated more than 30,000 candidate biomarkers from which less than a thousand might have clinical value [START_REF] Pritzker | Bioinformatics advances for clinical biomarker development[END_REF]. There is a vital issue in determining the relationship between biomarkers and relevant clinical endpoints, such as survival, stroke and myocardial infarction [START_REF] Strimbu | What are biomarkers?[END_REF]. Data mining techniques incorporating machine learning algorithms have been used as a possible path for solving this problem [START_REF] Dipnall | Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression[END_REF][START_REF] Swan | Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology[END_REF]. However, these are usually suited for large amounts of data which are not always available due to the complexity of measurement and quantity of biomarkers. In such cases, it is argued that defeasible reasoning might be considered a possible resolution technique.

In formal logics a defeasible concept is built upon a set of interactive pieces of evidence that can become defeated by additional reasons [START_REF] Pollock | Defeasible reasoning[END_REF]. Efforts have been made within the field of Artificial Intelligence (AI) to perform and analyze the act of reasoning defeasibly. Argumentation Theory (AT) is a computational approach which has been widely employed for modelling defeasible and nonmonotonic reasoning [START_REF] Bryant | A review of current defeasible reasoning implementations[END_REF]. It has been broadly applied in the field of health care [START_REF] Craven | Efficient argumentation for medical decision-making[END_REF][START_REF] Glasspool | Argumentation in decision support for medical care planning for patients and clinicians[END_REF] since data accounted in such problems is often uncertain, heterogeneous and incomplete. In this study the dataset employed follows this trend. It contains information of 93 patients and 51 different biomarkers collected in an European hospital from primary health care health records during the time span of five years. The survival of the patient is the dependent variable and indicates survival or death, while independent variables are both continuous and categorical. An initial investigation was performed and classification models such as decision trees could not provide accurate models, given the small amount of data. Thus, we proposed the investigation of computational argumentation theory for the prediction of survival in elderly. A knowledge-base constructed by a clinician was selected for examination. It adds dozens of rules and 7 different mortality risks. Its formalization was made by following the 5-layer modelling approach proposed by [START_REF] Longo | Argumentation for knowledge representation, conflict resolution, defeasible inference and its integration with machine learning[END_REF]. Finally, this schema was used for building a number of argument-based models with different parameters. Their accuracy and sensitivity (true positive rate) was subsequently computed for comparison purposes. The research question being investigated is: to which extent can defeasible reasoning, implemented using formal argumentation theory, enhance the prediction of survival in elderly using information on biomarkers according to accuracy and sensitivity?

The remainder of this paper is organized as follows. Section 2 presents related work on biomarkers and AT research, making the connection between the two areas. The design and methodologies of the study are detailed on section 3. Section 4 provides the comparison against decision trees and subsequent discussion. Finally, section 5 presents the conclusion and future work.

Related work

Research on prognostic information for mortality in older adults is of significant importance on clinical decision-making, for instance when deciding for more advance care planning for higher risks patients [START_REF] Lee | Development and validation of a prognostic index for 4-year mortality in older adults[END_REF]. Non-communicable diseases, such as cardiovascular, are the main cause of mortality among elderly individuals [START_REF] Lloyd-Jones | Heart disease and stroke statistics2009 update: a report from the american heart association statistics committee and stroke statistics subcommittee[END_REF]. Hence, the increase of biomarkers research to obtain early prognostics. Examples include [START_REF] De Ruijter | Use of framingham risk score and new biomarkers to predict cardiovascular mortality in older people: population based observational cohort study[END_REF][START_REF] Barron | Blood-borne biomarkers of mortality risk: systematic review of cohort studies[END_REF] which investigate possible biomarkers related to mortality. The predictive power of selected biomarkers is usually determined by a statistical analysis and so relies on data, not providing a complete explanation or a reasoning process. Recently, machine learning algorithms have also been used in this area of research [START_REF] Dipnall | Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression[END_REF][START_REF] Swan | Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology[END_REF]. Nonetheless, the validation of biomarkers as possible features of endpoints classification is still a concern on medical research [START_REF] Strimbu | What are biomarkers?[END_REF]. It falls on the same issues as other medical fields. In these, different pieces of information taken into account might be in contradiction with each other, thus a method for resolving them is often necessary. Knowledge-based systems are approach to deal with uncertainty and potentially valid for overcoming such issues [START_REF] Rizzo | Representing and inferring mental workload via defeasible reasoning: a comparison with the nasa task load index and the workload profile[END_REF][START_REF] Rizzo | Modeling Mental Workload Via Rule-Based Expert System: A Comparison with NASA-TLX and Workload Profile[END_REF]. These are well established within AI and have been used in several domains such as the pharmaceutical industry, clinical trials and care planning [START_REF] Slater | Beyond data integration[END_REF][START_REF] Hunter | Argumentation for aggregating clinical evidence[END_REF][START_REF] Longo | Formalising human mental workload as non-monotonic concept for adaptive and personalised web-design[END_REF][START_REF] Glasspool | Argumentation in decision support for medical care planning for patients and clinicians[END_REF][START_REF] Longo | Argumentation theory for decision support in healthcare: a comparison with machine learning[END_REF]. However, despite the similarity of the previous areas, it appears, from literature, that there is no research that attempts to employ defeasible reasoning in the biomarker domain for the prediction of survival. Reasoning is defeasible when a conclusion can be retracted in the light of new evidence. It is argued here that the knowledge required for modelling and assessing mortality risk using biomarkers can be seen as defeasible. It contains inconsistent pieces of evidence supporting different risk levels that are also retractable in the light of new information. Let us consider the following example with arguments:

-Arg 1: Increased mean cell volume of red blood cells (MCV) might lead to macrocytosis. Older people with macrocytosis are more likely to have poorer cognitive functioning and increased mortality. -Arg 2: Deficiency of vitamin B12 is an indicator of increased MCV. If there is no vitamin B12 deficiency then MCV can not be increased.

On one hand, in the first argument, a clinician may argue that there is evidence to infer an increased mortality risk, due to the increased measure of MCV which can lead to macrocytosis. In case there is no other evidence, increased mortality risk might be a reasonable conclusion. On the other hand, the second argument argues that if there is no deficiency of vitamin B12 then increased measure of MCV can not be taken into account. In this case the inference of an increased mortality risk no longer holds. This example illustrates how the set of conclusions does not increases monotonically and can be retracted in the light of new information. The next section explains how such arguments and conflicts can be represented and possible approaches to reach justifiable conclusions.

Design and methodology

This section illustrates how a knowledge-base on mortality risk factors in elderly was translated into computational argument-based models following a 5-layer schema [START_REF] Longo | A defeasible reasoning framework for human mental workload representation and assessment[END_REF]. Due to space limitation the full knowledge-base is not shown here, but it can be found online3 . It contains not only inference rules but also contradictions, preferences and a full description of all biomarkers utilized by the clinician, together with other 92 references used for the development of arguments (knowledge-base's rules).

Layer 1 -Definition of the structure of arguments

The first step on the argumentation process or the 5-layer schema is to define a set of forecast arguments. These can be represented like:

F orecast argument : premises → conclusion
The objective is to determine a premise or set of premises from which a conclusion can be reasonably inferred (survival or not). Survival was deducted from mortality risks which were in turn based on natural language expressions utilized by the clinician. Expressions like "may affect survival", " strong mortality risk factor" and "increased mortality" are a few examples. These expressions were separated in seven different mortality risks: no risk (r 1 ), low risk (r 2 ), medium low risk (r 3 ), medium risk (r 4 ), medium high risk (r 5 ), high risk (r 6 ) and extremely high risk (r 7 ). From the initial set of 51 biomarkers, 44 had a natural language description employable for the inference of some mortality risk. Let us consider a description for chronic obstructive pulmonary disease (COPD) and the possible argument associated to it:

-Description: COPD is a major cause of mortality and also a cardiovascular risk factor. There may be a survival benefit for treatment with new inhalatory drugs, however, conclusive data are currently lacking. -Arg: presence of COPD → extremely high risk (r 7 )

From the knowledge-base's descriptions it is possible to infer some mortality risk for some biomarkers, but the deduction from risks to survival or death is not known. Because of these two approaches are selected for investigation:

-Cautious: all risks are considered as potential predictors. r 1-3 are predictors for survival while r 4-7 are predictors for death. -Skeptical : only r 1-2 are associated with survival and r 6-7 with death. Arguments that support other conclusions (r 3-5 ) are no longer part of the reasoning process. Since risks r 3-5 are in the medium range it might be argued that they do not provide strong evidence to infer survival or not.

Layer 2 -Definition of the conflicts of arguments

At this layer the relationship between arguments is defined. While the first layer allows the definition of the monological structure of arguments, the second layer allows the creation of dialogical structures of knowledge. The objective is to support the examination of invalid arguments that have the earmarks of being legitimate. According to [START_REF] Matt | Combining statistics and arguments to compute trust[END_REF] these can be referred to as mitigating arguments.

Their internal structure is defined by a set of premises and an undercutting inference ⇒ to an argument B (forecast or mitigating):

M itigating argument : premises ⇒ B
A few classes of mitigating arguments can be found in [START_REF] Prakken | An abstract framework for argumentation with structured arguments[END_REF]. However, only one of them, undercutting attack, is adopted in this research for the implementation of conflicts. An undercutting attack occurs when there is a special case, or a defeasible inference rule, that does not allow the application of the knowledge carried in some other argument. Examples include expressions such as:

-Arg: If total cholesterol is high then hematocrite (HTC) is not low.

Based on this description an undercutting attack to a forecast argument (or a mitigating argument) can be built, that assumes low HTC as one of its premises. For instance, consider a forecast argument F1 and an undercutting attack UC1:

-F1: male and low HTC → medium low risk (r 3 ) -UC1: high total cholesterol ⇒ F1 Since it is known that HTC can not be low if total cholesterol is high, undercutting attack UC1 ensures that forecast rule F1 is not applied in this situation. A set of strict preferences among pairs of biomarkers for predicting survival are also provided by the clinician (expert) who was interviewed in this study. Such preferences can also be seen as undercutting attacks 4 . Consider the following preference P1, forecast arguments F2, F3 and an undercutting attack UC2:

-P1: Hypertension > Age -F2: Age ∈ [66, 70] → medium risk (r 4 ) -F3: high Hypertension → high risk (r 6 ) -UC2: F3 ⇒ F2

In the above example P1 suggests that hypertension is more important for inferring survival than age, thus forecast argument F3 should be considered instead of F2. Let us point out that forecast arguments F1 and F2 would not be defined according to the skeptical approach (layer 1) because their risks are not in the survival set (r 1-2 ) or death set (r 6-7 ), consequently UC1 and UC2 would also not be defined in this approach. Once layers 1 and 2 are finalized, the set or arguments and attacks can be seen now as a graph, or argumentation framework (AF). Figure 1 depicts AFs for cautions and skeptical approaches. Node labels follow the same names used for arguments in the full knowledge-base 5 .

Layer 3 -Evaluation of the conflicts of arguments

Once an AF is constructed, it can be elicited with data 6 . Some of arguments will be activated and kept, while others will not be activated and discarded. For instance, F1 premises do not evaluate true for female individuals or individuals that do not have low HTC, hence it is not activated and is discarded. Activated arguments might be in conflict, according to attacks defined in layer 2. These can be evaluated employing different strategies such as the strength of argument, preferentiality and strength of attack relations [START_REF] García | Strong and weak forms of abstract argument defense[END_REF]. Here attacks only present a form of a binary relation. In other words, once the arguments of an attack are activated, the attack is automatically considered efficacious. Activated arguments together with valid attacks form a sub-argumentation framework (sub-AF), that can now be evaluated against inconsistencies. Figure 2 depicts a sub-AF.

Layer 4 -Definition of the dialectical status of arguments

In order to accept or reject arguments, acceptability semantics are applied on top of each sub-AF. Note that each record in the dataset produces a different sub-AF, so semantics should be applied for each separated case. Most well known semantics, such as grounded and preferred, can be found in [START_REF] Dung | On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games[END_REF]. The goal in this layer is to evaluate not only if an argument is defeated but if the defeaters are defeated themselves. It is said that an argument A defeats argument B if and only if there is a valid attack from A to B. Dung's acceptability semantics [START_REF] Dung | On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games[END_REF] will generate one or more sets of extensions (conflict free sets of arguments). Each extension can be seen as a different point of view that can be used in a decision making process. At this stage the internal structure of arguments is not considered and in the literature this is known as an abstract argumentation framework (AAF). It is a pair < Arg, attacks > where: Arg is a finite set of (abstract) arguments, attacks ⊆ Arg × Arg is binary relation over Arg. Given sets X, Y ⊆ Arg of arguments, X attacks Y if and only if there exists x ∈ X and y ∈ Y such that (x, y) ∈ attacks. A set X ⊆ Arg of argument is -admissible iff X does not attack itself and X attacks every set of arguments Y such that Y attacks X; -complete iff X is admissible and X contains all arguments it defends, where X defends x if and only if X attacks all attacks against x; -grounded iff X is minimally complete (with respect to ⊆); -preferred iff X is maximally admissible (respect to ⊆)

It is important to highlight that arguments with different conclusions (survival or death in this study), are not necessarily conflicting (given the expert's knowledge-base) and might be part of the same extension. This is because they are originally defined according to the set of mortality risks (r 1-7 ) and not survival, thus, unless some inconsistency is explicitly defined, they can coexist. Nonetheless, in order to minimize this inconsistency, ranking-based semantics were also applied. This class of semantics is capable of distinguishing arguments not only as accepted or rejected, but it also provides a mechanism to fully rank them, therefore defining a form of importance. Here, the categorizer semantic is employed [START_REF] Besnard | A logic-based theory of deductive arguments[END_REF]. It takes into consideration the number of direct attackers to compute the strength of an argument. In this sense, attacks from non-attacked arguments are stronger and are more impactful then attacks from arguments attacked several times. Given an AAF =< Arg, attacks >, and the set of direct attackers att(a)∀a ∈ Arg, the categorizer function Cat : Arg →]0, 1] is given by:

Cat(a) =    1 if att(a) = ∅ 1 1+ c∈att(a) Cat(c) otherwise (1)
Finally, a ranking is given according to the value computed by Cat for each argument. Forecast arguments with different conclusions still might have the same ranking, for instance, if they are both not attacked. In this case another layer is required to accrue arguments and produce a final inference. Figure 3 illustrates grounded and categorizer semantics computed for the activated graph on figure 2. Note that forecast arguments attacked only by rejected arguments can still be rejected under the categorizer semantics.

Layer 5 -Accrual of acceptable arguments

The final step on the argumentation process of this study is to select one of the possible two outcomes: survival or death. As defined in layer 2 (section 3.2), two types of arguments can be part of an extension or have the same ranking: forecast and mitigating. Mitigating arguments, by definition, do not support any conclusion and have already finalized their role attacking other arguments and contributing to the definition of the set of acceptable arguments. Thus, the final inference is given only by the forecast arguments. Three situations are possible:

1. If the set of acceptable forecast arguments support the same conclusion, then final inference coincides to that conclusion. 2. If both conclusions are supported we argue that the one with the highest number of supporters should be chosen. Preferences have already been taken into account, hence the amount of evidence, or number of arguments supporting a conclusion, may be seen as a possible reason for choosing one. 3. If there is still a tie (survival and death being supported by an equal number of acceptable forecast arguments), then logically there is no reason for the system to take any conclusion. This might reflect the uncertainty of the domain, in which the knowledge-base, as coded from the expert in this study, does not contain information for dealing with this type of situation. Nonetheless, because one conclusion is eventually required for comparison purposes, the preference goes to death making a skeptical inference.

Table 1 lists the constructed models, summarising their settings for each layer.

Results

Collected data of 93 patients and 51 different biomarkers obtained from an European hospital from primary health care health records during the time span of five years7 was used to instantiate the designed argumentation-based models (table 1). In order to gauge the quality of the solutions an analysis was also performed using classification trees (CT) on the same dataset. Data was normalized and stratified 10-fold cross validation was applied for building CT models. The percentage of death and survival records is 39% and 61% respectively. The evaluation metrics selected were accuracy, obtained from each respective confusion matrix, and sensitivity or true positive rate. It is important to highlight that the comparison between argument-based inference (conducted case-by-case) and a learning technique (conducted on the whole dataset with CT) is not straightforward. In fact, argument-based models do not rely on data and consequently are not built on training sets. To be as fair as possible, oversampling could not be applied on the classification tree considering that synthetic data might be outside the knowledge-base capacity of understanding. The accuracy and sensitivity of each fold was computed across the argument-based models and the CT models (figures 4 and 5).

Accuracy

The accuracy of CT was higher than argument-based models in 4 folds, lower in 4 folds and equal in 2 folds. Given the size of the dataset, there is approximately 9 records on each fold, and because of that there is a large fluctuation in the accuracy percentage. Nonetheless, it is reasonable to say that, overall, tested approaches are nearly equivalent according to their accuracies (figure 4). Among the argument-based models, also no significant difference can be observed. This demonstrates a slight advantage of models M3-4 over M1-2, since the skeptical approach contain less information and could achieve similar results. Cautious models (M1-2) and skeptical models (M3-4) did not present any significant difference between them, suggesting that, given the topology of the argumentation frameworks (figure 1), grounded and categorizer semantics have no meaningful impact on inferences.

Sensitivity

Argument-based models could outperform CT in all folds. Only model M3 had a lower sensitivity in one fold. This shows how the techniques are equivalent based on their accuracies but argument-based models have a better prediction capacity for death, which is highly important in this domain. Among argumentbased models, folds 4 to 7 had the same sensitivity. Other folds presented a mixed prediction, sometimes having better results for models M1-2 and other times for models M3-4. Again, this suggests how the skeptical approach might be as predictive as the cautious one, making the use of medium risk biomarkers (by models M1-2) questionable for the prediction of survival.

Discussion

The analysis of accuracy of the investigated models demonstrates that the prediction capacity of survival by argument-based models is in line with the classification tree approach. Sensitivity, however, suggests that more precise inferences of death can be done by argument-based models. One can argue that learning algorithms might not be suited for the proportion of biomarkers by records in this problem (51 biomarkers and 93 records), thus another knowledge-base approach should be employed for comparison purposes. However, to the best of our knowledge, no other knowledge-base approach has been applied in the biomarker domain. In fact, the use of other knowledge-base techniques such as expert systems and fuzzy logic systems are mentioned as future work.

In line with this, it is important to mention the interest in pursuing explanation systems [START_REF] Core | Building explainable artificial intelligence systems[END_REF] in the health-care sector opposed to learning techniques where outcomes are not fully comprehended by humans. Defeasible reasoning and argument-based models allow a clearer reasoning process under uncertainty and incomplete data, are not based upon statistic or probability and allow the comparison of different knowledge-bases [START_REF] Longo | Argumentation theory in health care[END_REF]. Efforts have already been made in order to apply argument-based systems in the medical field [START_REF] Longo | Defeasible reasoning and argument-based systems in medical fields: An informal overview[END_REF], so it is expected that this integration might be enhanced by this research. For instance, take the knowledge-base applied here. Although it presents a large set of biomarkers it is not completely clear how they interact with each other. It is also not clear how the deduction from mortality risk to survival should be made. However, these are questions that are possibly not known even by domain experts. Nonetheless, two models or interpretations of this knowledge-base (cautious and skeptical) could be investigated and hopefully shed some light on these questions. For example, skeptical and cautious models presented similar performance, demonstrating that medium risk biomarkers did not have a significant contribution in the prediction of survival. Furthermore, each record analyzed has its own subargumentation framework, its own set of acceptable arguments and its own set of successful attacks. Such information can also be used for additional reasoning and improvement of biomarkers understanding towards survival.

Conclusion and future work

This research investigated the use of defeasible reasoning, formally implemented via computational argumentation theory, for the prediction of survival in elderly built upon evidence on biomarkers. The motivation for applying this technique comes from the lack of other studies employing knowledge driven approaches in the biomarker domain and also from the small amount of available data. A 5-layer schema was used to translate an extensive expert's knowledge-base into arguments. These were used to infer death or survival of elderly patients. The main disadvantage of building argument-based models is the time required to translate a knowledge-base generally expressed in natural language terms, into formal rules. Findings suggest that argument-based models are a promising avenue for the investigation of the relationship between biomarkers and survival in elderly. A comparison between argument-based models, built upon this 5-layer schema, and a classification tree algorithm showed that the formers can achieve a similar accuracy than the latter but a better sensitivity. In addition, argumentbased models are not based on statistic or probability and can reason with unclear and incomplete data. Future work will be focused on the application of different knowledge driven approaches, including expert systems and fuzzy logic. Moreover, given the uncertainty of the application (inference of survival of elderly using biomarkers) the same methodology of this study (5-layer schema) is going to be employed with different knowledge-bases designed by distinct experts.

Fig. 1 .

 1 Fig. 1. Argumentation framework: graphical representation of cautious (left) and skeptical (right) knowledge-bases. Double circles represent forecast arguments supporting survival (blue) or death (red). Other nodes represent premises of undercutting attacks. Edges are directed and represent attacks from an argument to another argument, or from a forecast argument to another forecast argument.

Fig. 2 .

 2 Fig. 2. Argumentation framework: an example of activated arguments (blue nodes inside) given one record of the dataset. Light nodes and edges are still part of the knowledge-base but are not considered for the next layers.

Fig. 3 .

 3 Fig. 3. Argumentation framework: acceptable arguments computed by the grounded semantics (left) and categorizer semantics (right). Blue nodes inside are activated but do not support a conclusion, so are not accepted neither rejected. Red and green nodes inside are forecast arguments rejected and accepted respectively. Double circles indicate whether forecast arguments support death (red) or survival (blue).

Fig. 4 .Fig. 5 .

 45 Fig. 4. Accuracy of classification tree (CT) and each model (M) by fold.

Table 1 .

 1 Set up of each model investigated. For layers 1, 2 and 4 (L1, L2, L4 ) a parameter is set. Layers 3 and 5 are equal for all cases.

	Model	Survival (L1 )	AF (L2 )	Semantics (L4 )
	M1	Cautious	figure 1 (left)	Grounded
	M2	Cautious	figure 1 (left)	Categorizer
	M3	Skeptical	figure 1 (right)	Grounded
	M4	Skeptical	figure 1 (right)	Categorizer

http://dx.doi.org/10.13140/RG.2.2.20905.49764

Note that preference among biomarkers is not the same as preferentiality of arguments, a notion mentioned on layer 3.

5 http://dx.doi.org/10.13140/RG.2.2.20905.49764

http://dx.doi.org/10.13140/RG.2.2.17130.62402

http://dx.doi.org/10.13140/RG.2.2.17130.62402

Acknowledgments

Lucas Middeldorf Rizzo would like to thank CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) for his Science Without Borders scholarship, proc n. 232822/2014-0.


Aliaksandr Barushka
email: aliaksandr.barushka@student.upce.cz

Petr Hajek
email: petr.hajek@upce.cz

Spam Filtering in Social Networks using Regularized Deep Neural Networks with Ensemble Learning

Keywords: neural network, social networks, regularization, meta-learning

Spam filtering in social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machine and Naïve Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. To overcome this problem, here we propose a novel approach to social network spam filtering. The approach uses ensemble learning techniques with regularized deep neural networks as base learners. We demonstrate that this approach is effective for social network spam filtering on a benchmark dataset in terms of accuracy and area under ROC. In addition, solid performance is achieved in terms of false negative and false positive rates. We also show that the proposed approach outperforms other popular algorithms used in spam filtering, such as decision trees, Naïve Bayes, artificial immune systems, support vector machines, etc.

Introduction

Generally, spam can be defined as unwanted and unsolicited messages sent to usually a large number of recipients [START_REF] Cormack | Email spam filtering: A systematic review[END_REF]. Spam message can be sent over multiple communication channels, such as e-mail, SMS, social networks, etc. Statistics show that a large proportion of all messages in social networks are spam messages. For instance, the study by Proofpoint, a major company specialized in cyber security, reported that during the first half of 2013 there has been a 355% growth of social spam. For every seven new social media accounts, five new spammers are detected [START_REF] Nexgate | State of Social Media Spam[END_REF].

The growing opportunities of social networks and their popularity have attracted many users. These days the base of social network users is steadily growing and considerable amount of communication is done through social networks. However, along with legitimate and useful information, inappropriate and unwanted content is also released on these networks. Indeed, spam senders target social network users as well. Moreover, business social networks like Linkedin are also affected [START_REF] Prieto | Detecting linkedin spammers and its spam nets[END_REF]. This has serious economic and social consequences. Spam messages decrease work productivity, increase IT support related resources (help desk) and may even result in security incidents. This is why a considerable attention is given to spam filtering.

Spam messages can be filtered either manually or automatically. Obviously, manual spam filtering by identifying spam message and removing it is a time consuming task. Moreover, spam messages may contain a security threat, such as links to phishing web sites or servers hosting malware. Therefore, over a number of decades researches and practitioners have worked on improving automatic spam filtering algorithms. Machine learning techniques are particularly known to be highly accurate in detecting spam messages. There is a number of existing machine learning algorithms applied to spam filtering, including neural networks [START_REF] Barushka | Spam filtering using regularized neural networks with rectified linear units[END_REF], support vector machines (SVMs) [START_REF] Bhowmick | E-mail spam filtering: A review of techniques and trends[END_REF], Naïve Bayes [START_REF] Almeida | Spam filtering: how the dimensionality reduction affects the accuracy of Naive Bayes classifiers[END_REF], random forest [START_REF] Choudhary | Towards filtering of SMS spam messages using machine Learning Based Technique[END_REF], etc.

Spam filtering task belongs to binary classification problem, each message should be identified either as spam or ham. Besides high accuracy algorithm should also perform well when it comes to false positive ratio (legitimate message is classified as spam) to avoid situations where legitimate message is not delivered to the intended receiver. The main concept of the machine learning algorithms is to build a word list and assign a weight to each word accordingly. However, spammers tend to include common legitimate messages into the spam message in order to decrease the probability of being detected. In social network spam filtering, additional attributes are therefore used, such as those related to sender's profile and behavior in the social network.

The state-of-the-art methods in social network spam filtering has recently been surveyed by [START_REF] Kaur | Spam detection on Twitter: A survey[END_REF]. According to the survey, ensemble learning methods, such as bagging and random forest, outperform traditional single classifiers. The ensemble methods combine the predictions of several base machine learning algorithms in order to improve accuracy and robustness over single algorithms. In previous studies, ensemble methods employed traditional classifiers like decision trees to effectively filter spam messages. However, surprisingly little attention has been paid to neural networks with ensemble learning. Recent evidence showed that neural networks equipped with regularization techniques may be highly accurate in detecting e-mail and SMS spam [START_REF] Barushka | Spam filtering using regularized neural networks with rectified linear units[END_REF]. This can be attributed to better optimization convergence and resistance to overfitting. To take advantage of these qualities, here we integrate regularized neural networks with ensemble learning methods for social network spam filtering. Using the benchmark data from the Dutch social networking site Hyves, here we show that this approach can be more effective than state-of-the-art spam filtering methods in terms of accuracy.

The rest of this paper is organized in the following way. Section 2 briefly reviews recent development in social network spam filtering. Section 3 presents the benchmark dataset. In section 4, we introduce the proposed spam filter. Section 5 shows the results of the experiments and a comparative analysis with several state-of-the-art methods used for social network spam filtering. In section 6, we conclude this paper and discuss possible future research directions.

Social Network Spam Filtering -A Literature Review

User base of social networks is growing over the number of years. For instance, Facebook, one of the biggest social networks in the world, grew from one billion to two billion users just in 5 years [9]. Social network spam has become a major concern of industry and academia because it may include unwanted content, such as insults, hate speech, malicious links, etc. Such messages can be seen by the recipient's followers. Moreover, they may lead to confusions and misdirection in public discussions [START_REF] Zheng | Detecting spammers on social networks[END_REF]. Fighting social network spam with traditional legal methods has serious limitation, since spam messages in social networks can be sent from different countries. It is important to note that spammers may use anonymisers, making it difficult to trace them. In order to overcome this problem, several social network spam filters have recently been developed [START_REF] Zheng | Detecting spammers on social networks[END_REF][START_REF] Wang | Don't follow me: Spam detection in Twitter[END_REF][START_REF] Benevenuto | Detecting spammers on twitter[END_REF][START_REF] Aswani | Detection of spammers in twitter marketing: a hybrid approach using social media analytics and bio inspired computing[END_REF][START_REF] Shen | Discovering social spammers from multiple views[END_REF][START_REF] Gogoglou | Early malicious activity discovery in microblogs by social bridges detection[END_REF][START_REF] Bindu | Discovering spammer communities in twitter[END_REF][START_REF] Wu | Co-detecting social spammers and spam messages in microblogging via exploiting social contexts[END_REF][START_REF] Zheng | ELM-based spammer detection in social networks[END_REF][START_REF] Yu | Constrained NMF-based semi-supervised learning for social media spammer detection[END_REF][START_REF] Bosma | A framework for unsupervised spam detection in social networking sites[END_REF][START_REF] Barushka | Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks[END_REF][START_REF] Dutta | Attribute selection for improving spam classification in online social networks: a rough set theory-based approach[END_REF].

A Naïve Bayes classifier was proposed by [START_REF] Wang | Don't follow me: Spam detection in Twitter[END_REF] to detect spam in Twitter. Features related to tweet content and user behaviour were identified and used in machine learning by [START_REF] Benevenuto | Detecting spammers on twitter[END_REF]. A hybrid approach for identifying spam profiles was proposed by [START_REF] Aswani | Detection of spammers in twitter marketing: a hybrid approach using social media analytics and bio inspired computing[END_REF], combining social media analytics and firefly algorithm with chaotic maps for spam detection in Twitter marketing. In addition to spam messages detection, recent studies have also considered an alternative task of social spammer detection. A large Twitter dataset was used in [START_REF] Shen | Discovering social spammers from multiple views[END_REF] to demonstrate that feature distributions between spammers and legitimate users are different. These feature distributions were used in a social spammer detection framework that integrated this information with a social regularization term incorporate into a classification model. In [START_REF] Gogoglou | Early malicious activity discovery in microblogs by social bridges detection[END_REF], the so-called "social bridges" were identified to detect spammers in Twitter. These are reported as the major supporters of malicious users and a graph-topology based classifier was used to detect such bridge linkages. Another way to tackle the issue of detecting spammers in Twitter was described in [START_REF] Bindu | Discovering spammer communities in twitter[END_REF]. A multilayer social network was defined and the identification of spammers was based on the existence of overlapping community-based features of users represented in the form of Hypergraphs, such as structural behaviour and URL characteristics. A unified approach was proposed in [START_REF] Wu | Co-detecting social spammers and spam messages in microblogging via exploiting social contexts[END_REF], utilizing the fact that social spammers tend to post more spam messages. Indeed, it was shown that combining social spammer filtering with spam message filtering improves the performance of both tasks.

Although Twitter represents the most frequently used source of data, alternative social networks have also been examined. For example, data from Sina Weibo were used to study features related to message content and user behaviour in [START_REF] Zheng | Detecting spammers on social networks[END_REF]. The most important features were then used in the SVM classifier for spam detection. Extreme learning machines were used by [START_REF] Zheng | ELM-based spammer detection in social networks[END_REF] on a similar dataset. A semi-supervised social media spammer filtering approach was developed in [START_REF] Yu | Constrained NMF-based semi-supervised learning for social media spammer detection[END_REF]. This approach outperformed traditional supervised classifiers for the spammer detection task. Similar results were obtained by for spam message detection in Hyves social network [START_REF] Bosma | A framework for unsupervised spam detection in social networking sites[END_REF]. Using the same dataset, significant improvements were achieved by combining data oversampling with regularized deep multi-layer perceptron neural networks [START_REF] Barushka | Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks[END_REF].

Several researchers employed feature selection and extraction methodologies to identify the most important features for social network spam filtering. The concept of rough set theory was applied by [START_REF] Dutta | Attribute selection for improving spam classification in online social networks: a rough set theory-based approach[END_REF], concluding that the used methodology selected a smaller subset of features than those of the baseline methodologies. By considering important features of the posts and their corresponding comments, and finally applying the feature selection techniques, the method proposed in [START_REF] Sohrabi | A Feature selection approach to detect spam in the Facebook social network[END_REF] selected the most effective features to detect spam using machine learning techniques. A probabilistic generative model (latent Dirichlet allocation) was proposed by [START_REF] Song | Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection[END_REF] to detect the latent semantics from user-generated comments. Incremental learning was then used to address the issue of the changing feature space.

In summary, previous related literature attempted to overcome the problem of highdimensional data (the curse of dimensionality) by selecting the most important features, regardless of whether content-based features or user behaviour features. This was mainly due to the risk of overfitting or poor convergence of the used classification methods. However, useful information may be hidden in higher-order features that can be extracted by using deep neural networks [START_REF] Barushka | Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks[END_REF]. In fact, additional hidden layers enable the recombination of features and thus to capture higher complexity and abstraction in high-dimensional datasets [START_REF] Hinton | Improving neural networks by preventing co-adaptation of feature detectors[END_REF]. Moreover, ensemble methods have become popular in social network spam detection tasks due to their capacity to reduce the risk of overfitting and variance [START_REF] Kaur | Spam detection on Twitter: A survey[END_REF].

Dataset

In this study, we used the dataset from Hyves, the Dutch social networking site 1 . The original dataset contained both labelled and unlabelled messages. As we use a supervised learning approach here, we excluded the unlabelled (unannotated) messages from the dataset. Since the details on this dataset can be found in [START_REF] Bosma | A framework for unsupervised spam detection in social networking sites[END_REF], we provide only a brief description in this study. The dataset includes the following types of information: message content, spam report and user information. The messages were collected from publicly accessible profile or group pages. At least two spam reports were created for each message to obtain reliable categorization of messages into "spam" or "not spam". The user policy of Hyves was used to define spam messages. Specifically, unsolicited and promotional messages were labelled as spam.

The social network spam dataset contained 466 spam messages and 355 legitimate messages. The messages were represented as the arrays of json objects with the following fields: the annotation of the object (either spam or legitimate), anonymized IDs of the reporters of the message, anonymized ID of the author of the message, and bag of words representation of the message (an anonymized ID was assigned to each word). To represent the bag of words, we used tf.idf weighting scheme. The weight vij for the i-the word in the j-th message can be calculated as follows:

vij = (1 + log(tfij)) × log(N/dfi), (1)
where tfij represents term frequency, dfi is document frequency, and N is the number of messages. We used top 2000 words according to their weights. This number was reported to be sufficient to perform document classification in previous studies [START_REF] Dhillon | A divisive information-theoretic feature clustering algorithm for text classification[END_REF].

Methods

Since the main interest of the paper is the proposal of a social network spam filter based on deep neural network with ensemble learning, we provide a brief description of these methods in this section.

The model of the deep neural network (DNN) used in this study is the multilayer perceptron neural network with multiple hidden layers that process complex relations between the input features and output categories. However, such structure results in the large number of connections, leading to sampling noise. Therefore, intensive adaptation of training data may result in overfitting. To address this issue, we used dropout regularization. Indeed, increased accuracy may be achieved by dropping units from the neural network, including all their incoming and outgoing connections. The dropout regularization randomly changes the given ratio of the activations' values to zero while training is performed and therefore hidden units that produce the same result are ignored. In addition, we employed rectified linear units instead of traditional sigmoidal units in order to avoid poor local minima of training error and slow optimization convergence [START_REF] Maas | Rectifier nonlinearities improve neural network acoustic models[END_REF]. This is done by producing partial derivative equal to one, in case the rectified linear unit is activated. It is also worth to add that these units saturate when reaching one. This might be useful when hidden activations are selected as input features for the classifier. The mini-batch gradient descent was used as a training algorithm for the DNN. Connection weights are updated for every mini-batch of training data in the following way: wt+1 = wt -η∇θJ(wt; x (i:i+n) ; y (i:i+n) ),

(

) 2
where w is connection weight, t denotes time, η is learning rate, J is an objective function, x i and y i are the inputs and output of the i-the data sample within every mini-batch, and n is the number of data samples in the mini-batch. By using the mini-batches a stable convergence can be achieved during the DNN learning. The goal of ensemble learning algorithms is to combine the predictions of multiple base estimators constructed with the defined learning algorithm. This approach leads to better generalizability and robustness over single estimators. There are two main classes of ensemble learning algorithms, averaging and boosting. The fundamental concept of averaging is to construct several estimators independently from each other and calculate the average of their predictions. By reducing variance, the combined estimator is more accurate than single base estimator. By contrast, boosting builds the base estimators sequentially. Thus, several sequential weak models are combined to achieve a good ensemble. Here we used three conventional ensemble learning algorithms, namely Adaboost M1 [START_REF] Freund | Experiments with a new boosting algorithm[END_REF], bagging [START_REF] Breiman | Bagging predictors[END_REF] and random subspace [START_REF] Ho | The random subspace method for constructing decision forests[END_REF].

The Adaboost M1 algorithm was developed to produce predictions with high accuracy utilizing a number of weak base learners. The algorithm keeps building the learners until there are no errors in training data predictions or the limit numbers of models is exceeded. This is done by increasing the weights of incorrectly predicted data. Finally, the predictions from all the models are combined by using a weighted majority vote to obtain the final predictions. The algorithm is defined as follows: The main idea behind bagging is to construct multiple instances of black-box estimator on the random subsets of the original training data. To produce an aggregated prediction, separate predictions are then combined by using the voting procedure. Thus, the variance of base estimator is reduced by applying randomization during the process of building ensembles. The bagging algorithm employed here can be defined as follows: Random subspace algorithm was proposed to handle the problem of trade-off between overfitting and achieving the highest accuracy. In fact, the random subspace algorithm is similar to bagging. The main difference is in the way they draw the random subsets of training data. In random subspace, these subsets are produced as the random subsets of the features. The random subspace algorithm applied here for social network spam filtering can be defined as follows:

Experimental Results

In this section, we first describe the setting of all experiments and then we present the results in terms of four prediction measures: accuracy, area under ROC (receiver operating characteristic) curve, FN (false negative) rate and FP (false positive) rate, and F1score. FN rate represents the percentage of spam messages incorrectly predicted as legitimate, while FP rate is the percentage of legitimate messages incorrectly predicted as spam. F1-score combines precision and recall, where precision is a fraction of messages correctly classified as spam out of all the messages the algorithm classifies as spam, whereas recall is the fraction of messages correctly classified as spam out of all the spam messages. 10-fold cross-validation was used to avoid overfitting and evaluate the prediction performance.

The DNN with ensemble learning was trained with the following setting: number of hidden layers = {1, 2, 3}, number of units in hidden layers = {10, 20, 50}, and learning rate set to = 0.1, size of mini-batches = 100, dropout rate for input layer = 0.2; dropout rate for hidden layers = 0.5, and number of iterations = 1000. The best setting of the DNN structure (2 hidden layers with 50 and 20 units, respectively) was obtained by using grid search. Furthermore, 10 iterations were used in the learning of Adaboost M1 ensemble, the size of each bag in bagging was 100, and the size of each subspace was 50% of all attributes in the random subspace algorithm. The learning of bagging and random subspace was also performed in 10 iterations.

To demonstrate the effectiveness of the proposed social network spam filter, we compared its performance with several methods used in previous studies for spam filtering [START_REF] Barushka | Spam filtering using regularized neural networks with rectified linear units[END_REF][START_REF] Bhowmick | E-mail spam filtering: A review of techniques and trends[END_REF][START_REF] Almeida | Spam filtering: how the dimensionality reduction affects the accuracy of Naive Bayes classifiers[END_REF][START_REF] Choudhary | Towards filtering of SMS spam messages using machine Learning Based Technique[END_REF][START_REF] Kaur | Spam detection on Twitter: A survey[END_REF], namely the single DNN, CNN (convolutional neural network), Naïve Bayes, k-NN (k nearest neighbour), C4.5 decision tree, MLP (multilayer perceptron), SVM (support vector machine), AIRS (artificial immune recognition system), Adaboost M1 with decision stump as base learner, and random forest. The settings of these algorithms were as follows: single DNN (the same setting as for the DNN with ensemble learning); CNN (mini-batch gradient descent algorithm with patch size 5×5 and max pool size 2×2, the remaining parameters were the same as for the DNN); k-NN (k = 3); C4.5 (J48 implementation with the confidence factor of 0.25 and minimum instances per leaf = 2); MLP (backpropagation with {10, 20, 50, 100} units in the hidden layer (50 units worked best), learning rate = 0.1, momentum = 0.2, and iterations = 1000); SVM (sequential minimal optimization algorithm with C = {2 0 , 2 1 , … , 2 6 } (C = 2 2 worked best) and polynomial kernel function); AIRS (AIRS2 parallel algorithm with affinity threshold = 0.2, clonal rate = 10, hyper-mutation rate = 2, k = 3 and stimulation threshold = 0.9); Adaboost M1 with 10 iterations and decision stump as base learner; and 100 random trees were used in random forest. All the experiments were performed in Weka 3.7.13 environment.

A brief description of the comparative methods is given as follows: CNN uses layers together with convolving filters and filters are applied to the local features of adjacent layers. Each hidden layer consists of several feature maps (filters in a layer build a feature map sharing the same parametrization). Max-pooling is used to capture the most important features for each feature map. NB classifier utilizes information learnt from training data in order to calculate the probability of spam or legitimate class taking into consideration words found in the message. In k-NN, the message is classified based on the most common class in k neighbours. J48 algorithm generates a decision tree model, including variable classification rates built on cross-validation. MLP is a feed-forward neural network that consists of several layers of units, namely input, hidden and output layer. Each layer is directly connected to the next layer in the MLP. Backpropagation algorithm is commonly used to train this neural network. In contrast to empirical risk considered in the training of the MLP, SVM learning is based on structural risk minimization. Specifically, SVM finds the optimal separating hyperplane that represents the maximum margin between two classes and the corresponding decision are defined by the so-called support vectors. As a result, this algorithm can effectively handle high-dimensional data. AIRS is another artificial intelligence approach. This algorithm includes models resembling particular immunological processes. Finally, random forest consists of multiple tree predictors. Each of them is influenced by the values of an independently sampled random vector. Therefore, all the trees in the forest share the same distribution.

The results of the experiments are summarized in Table 1 and Table 2. The results show that the DNN with bagging performed best in terms of accuracy and area under ROC curve. Besides the DNN with ensemble learning, the single DNN, CNN and random forest also performed well. Student's paired t-tests were performed to compare the results statistically. The results that are statistically similar to the best performer at p=0.05 are in bold in Table 1 andTable 2 The results in Table 2 show that the DNN with ensemble learning performed well in terms of FN rate, while relatively poorly in terms of FP rate. Overall, however, the performance was well balanced for both spam and legitimate classes. This was also confirmed with the high values of the area under ROC curve. Moreover, the DNN with bagging and DNN with random subspace performed best in terms of F1-score, indicating a balanced performance in both the precision and the recall. Notably, we obtained better results than those reported in the original study using this dataset (ROC = 0.801) [START_REF] Bosma | A framework for unsupervised spam detection in social networking sites[END_REF]. However, this original study was based on a simple spam score combining messages' own and neighbour characteristics. More precisely, the best performance in terms of accuracy was achieved by using the DNN with bagging. As presented in Table 2, this can be mainly attributed to the low value of FN rate. In other words, this method performs particularly well in predicting the spam class. The DNN with adaboost performed even better in terms of this criterion but it failed to classify the legitimate messages compared with the remaining ensemble methods. Finally, the DNN with random subspace performed reasonably well with respect to both classes, resulting in high accuracy and area under ROC curve. Regarding the other comparative methods, they were significantly outperformed by the best spam filter in terms of accuracy, except random forest, CNN and DNN. Although the improvement in accuracy might not seem substantial (e.g., less than one percent over random forest), real-life spam filters achieve up to 99.9% accuracy. Every improvement is therefore highly warranted. However, some of the comparative methods were good in detecting one of the classes. Specifically, Naïve Bayes was best in detecting the social network spam class, while Adaboost M1 performed best on the legitimate class of messages. On the other hand, Adaboost M1 cannot be recommended for spam filtering in social networks due to the low value of the area under ROC curve, reflecting the poor performance on the spam class.

Conclusion

In this study, we demonstrated that ensemble learning algorithms with DNN as the base learner is more accurate than state-of-the-art spam filtering methods. The results show that bagging algorithm trained with DNNs achieved best results, with a high accuracy on both classes. This can be attributed to the capacity of bagging in reducing the risk of overfitting. In fact, bagging performs best with complex base learners, just like DNNs. Note that this is different from boosting where weak base learners are preferred. Moreover, reducing the number of features with random subspace does not seem to be beneficial in case of DNNs. To sum up, the combination of complex DNNs trained on random subsets of high-dimensional data seems to be an effective method for social network spam filtering. On the other hand, ensemble learning algorithms with DNN performed relatively poorly when it comes to FN rate.

Several limitations of this study need to be mentioned. Here we used the content of the messages, together with the information about the author (the number of messages authored) and reporter (the number of messages reported). However, the content of the neighbouring messages could be utilized in future studies. This would require a larger dataset to be collected. This model could also be used to predict spammers in addition to spam messages. Recent studies showed that such a combination might significantly improve the accuracy of spam and spammer filters.

The results obtained here suggest that DNNs with ensemble learning might have great potential also in other text categorization tasks, such as web-page classification, e-mail spam filtering, sentiment classification and so forth. It would also be interesting to investigate the effect of different feature selection approaches and therefore further investigation and experimentation is highly recommended. Moreover, it would be beneficial to investigate whether described methods show similar performance for spam datasets from different countries in different languages from different social networking platforms and whether localization of classification algorithm is required.

Algorithm 1 :

 1 Adaboost M1 Input: The set T of training data (x i ; y i ), i=1,2, … ,n; the number B of base DNNs Output: Ensemble of base DNNs {Cb} For b=1 to B { Construct a base DNN Cb on weighted training data T*=(w1T 1 b, w2T2 b, … , wnT n b); Calculate the probability estimates of the error errb=1/n Σ wib×ξ i b (ξ i b=0 if T i classified correctly, ξ i b=1 otherwise); Set weight cb=0.5×log((1-errb)/errb); If errb<0.5, set wib+1=wib×exp(cbξ i b); Otherwise, set all weights wib=1 and restart the algorithm; } Combine base DNNs Cb, b=1,2,…,B into an ensemble {Cb} by weighted majority voting;

Algorithm 2 :

 2 Bagging Input: The set T of training data (x i ; y i ), i=1,2, … ,n; the number B of base DNNs Output: Ensemble of base DNNs {Cb} For b=1 to B { Create a bootstrapped replicate Tb of the training data set T; Construct a base DNN Cb on Tb; } Combine base DNNs Cb, b=1,2,…,B into an ensemble {Cb} by simple majority voting;

Algorithm 3 :

 3 Random subspace Input: The set T of training data (x i ; y i ), i=1,2, … ,n; the number B of base DNNs Output: Ensemble of base DNNs {Cb} For b=1 to B { Select an r-dimensional random subspace Tb from the original training data set T; Construct a base DNN Cb in Tb; } Combine base DNNs Cb, b=1,2,…,B into an ensemble {Cb} by simple majority voting;

Table 1 .

 1 . Results of the experimentsaccuracy and area under ROC curve.

	Method	Accuracy	Area under
		[%]	ROC curve
	Naïve Bayes	82.98±7.08	0.943±0.026
	k-NN	88.37±3.71	0.946±0.026
	C4.5	88.65±3.84	0.912±0.037
	MLP	88.97±3.86	0.942±0.027
	SVM	90.45±3.25	0.907±0.032
	AIRS	80.42±7.78	0.914±0.033
	Adaboost M1	89.10±3.73	0.907±0.034
	Random forest	91.94±3.09	0.960±0.023
	CNN	91.11±4.52	0.950±0.051
	DNN	92.27±3.04	0.961±0.021
	DNN with adaboost	90.87±2.87	0.940±0.027
	DNN with bagging	92.69±2.82	0.962±0.022
	DNN with random subspace	92.45±3.08	0.961±0.023

Table 2 .

 2 Results of the experiments -FN and FP rates.

	Method	FN rate [%] FP rate [%]	F1-score
	Naïve Bayes	6.1±3.8	30.2±19.5	0.8641±0.0461
	k-NN	16.1±6.0	34.0±3.0	0.8903±0.0375
	C4.5	14.0±5.5	5.7±4.3	0.8953±0.0369
	MLP	11.6±17.0	6.8±6.1	0.8978±0.0364
	SVM	10.8±4.9	5.1±3.8	0.9131±0.0306
	AIRS	25.4±14.5	11.9±18.5	0.8415±0.0460
	Adaboost M1	17.1±6.0	2.8±2.5	0.8952±0.0388
	Random forest	10.0±4.7	5.1±3.6	0.9264±0.0292
	CNN	9.2±4.6	5.2±4.2	0.9211±0.0338
	DNN	9.2±4.3	3.4±3.0	0.9292±0.0292
	DNN with adaboost	8.8±5.4	9.6±2.8	0.9184±0.0273
	DNN with bagging	9.0±3.6	5.1±2.9	0.9338±0.0258
	DNN with random subspace	10.1±4.5	4.2±2.4	0.9308±0.0211

http://ilps.science.uva.nl/framework-unsupervised-spam-detection-social-networking-sites/

Acknowledgments. This article was supported by the by the grant No. SGS_2018_019 of the Student Grant Competition.