Learning and clustering of fuzzy cognitive maps for travel behaviour analysis

León, Maikel; Mkrtchyan, Lusine; Depaire, Benoît; Ruan, Da; Vanhoof, Koen

doi:10.1007/s10115-013-0616-z

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…Roderguz et al proposed a fuzzy ontology for semantic modelling and recognition of human behavior [12], and the main contribution was to help modeling and treating uncertain, vague, incomplete, or imprecise information. Leon et al used learning and clustering of fuzzy cognitive maps to describe travelers' behavior and change trends in different abstraction levels [13], the results of this work help transportation policy decision makers in better understanding of people's needs. Dai and al. developed a new algorithm, in 2020, combined association rules and multivalued discrete features, association rules (AR) are used to calculate the jaccard distance (ARJD).…”

Section: Related Workmentioning

confidence: 99%

Human behavior scoring in credit card fraud detection

Sadgali¹,

Sael²,

Benabbou³

2021

IJ-AI

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<span lang="EN-US">Now days, the analysis of the behavior of cardholders is one of the important fields in electronic payment. This kind of analysis helps to extract behavioral and transaction profile patterns that can help financial systems to better protect their customers. In this paper, we propose an intelligent machine learning (ML) system for rules generation. It is based on a hybrid approach using rough set theory for feature selection, fuzzy logic and association rules for rules generation. A score function is defined and computed for each transaction based on the number of rules, that make this transaction suspicious. This score is kind of risk factor used to measure the level of awareness of the transaction and to improve a card fraud detection system in general. The behavior analysis level is a part of a whole financial fraud detection system where it is combined to intelligent classification to improve the fraud detection. In this work, we also propose an implementation of this system integrating the behavioral layer. The system results obtained are very convincing and the consumed time by our system, per transaction was 6 ms, which prove that our system is able to handle real time process.</span>

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Section: Related Workmentioning

confidence: 99%

Human behavior scoring in credit card fraud detection

Sadgali¹,

Sael²,

Benabbou³

2021

IJ-AI

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“…The number of views in the evaluation can be very large. León et al (2014), for example, evaluated more than 100 opinions in his analysis. This type of analysis is not useful for deeper analysis.…”

Section: Aggregation Of Individual Mapsmentioning

confidence: 99%

“…The mathematical clustering methods always run the risk of making mistakes (León et al, 2014). Therefore, it is recommended that they be used cautiously.…”

Section: Aggregation Of Individual Mapsmentioning

confidence: 99%

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Aggregation cognitive maps procedure for group decision analysis

2016

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Purpose -Cognitive maps are used in group decision processes to structure problems. The problem structuring methods helps decision makers to improve the comprehension of the problem, identifying alternative actions and conflicts. However, represents the individual perceptions in a representative group decision into a single structure can be a complex task. The paper aims to discuss these issues. Design/methodology/approach -The objective of this paper is to improve the process of discussion, obtaining the interests and views of the participants and provide parameters to assist the analyst to guide the process. Furthermore, it is possible to analyze how participants are aligned or diverge from the group. The literature review presents some approaches for cognitive maps analysis, but there is a lack of structured methods to analyze them. This paper proposes a structure procedure for the aggregation of cognitive maps in three parts: workshop to generate individual maps, the aggregation of individual maps and the refinement of the global map. Findings -An example illustrates the application of the proposed method and shows the construction of a global map that summarizes the concepts that participants consider important. Originality/value -This paper presents a new procedure that allows reducing the bias of the analyst in the aggregation of individual cognitive maps maintaining the relevant information and allows decision makers know and approve the aggregation procedure.

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“…In this section we analyse the results of clustering FCMs before and after learning [117]: thus we investigate traveller"s preferences and similarities when they choose only the nodes and the weighted links (first case) and when in addition they evaluate also different scenarios (second case). Figure 10 shows the general approach overview and results of using clustering and learning of FCMs to analyse users" preferences in different stages of reasoning.…”

Section: Decision Making With Different Levels Of Abstractionmentioning

confidence: 99%

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

León¹,

Nápoles²,

Bello³

et al. 2013

IJCIS

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Although the individuals" transport behavioural modelling is a complex task, it can produce a notable social and economic impact. In this paper, Fuzzy Cognitive Maps are explored to represent the behaviour and operation of such complex systems. An automatic approach to extract mental representations from individuals and convert them into computational structures is defined. For the creation of knowledge bases the use of Knowledge Engineering is accounted and later on the data is transferred into structures based on Fuzzy Cognitive Maps. Once the maps are created, their performances get improved through the use of a Particle Swarm Optimisation algorithm as a learning method, readjusting its predicting capacity from stored scenarios, where individuals left their preferences in front of random situations. Another important result is clustering the maps for knowledge discovery. This permits to find useful groups of individuals that policymakers can use for simulating new rules and policies. After related maps are identified, to merge them as a unique structure could benefit for different usages. Therefore an aggregating procedure is elaborated for this task, constituting an alternative approach for selecting a centroid of a specific estimated group, and therefore having, in only one structure, the knowledge and behavioural acting from a collection of individuals. Learning, clustering and aggregation of Fuzzy Cognitive Maps are combined in a cascade experiment, with the intention of describing travellers" behaviour and change trends in different abstraction levels. The results of this approach will help transportation policy decision makers to understand the people"s needs in a better way, consequently will help them actualising different policy formulations and implementations.

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Learning and clustering of fuzzy cognitive maps for travel behaviour analysis

Cited by 11 publications

References 36 publications

Human behavior scoring in credit card fraud detection

Human behavior scoring in credit card fraud detection

Aggregation cognitive maps procedure for group decision analysis

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

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