Two-Stage Learning based Fuzzy Cognitive Maps Reduction Approach

Miklos Ferenc Hatwagner, Engin Yesil, Furkan Dodurka, Elpiniki I. Papageorgiou, Leon Urbas, L. Kóczy

Research output: Contribution to journalArticle

5 Citations (Scopus)


In this study, a new two-stage learning based reduction approach for Fuzzy Cognitive Maps (FCM) is introduced in order to reduce the number of concepts. FCM is a graphical modeling technique that follows a reasoning approach similar to the human reasoning and decision-making process. The FCM model incorporates the available knowledge and expertise in the form of concepts and in the direction and strength of the interactions among concepts. One of the modeling problems of FCMs is that over-sized FCM models suffer from interpretability problems. An over-sized FCM may contain concepts that are semantically similar and affects the other concepts in a similar way. This new study introduces a two-stage model reduction approach, and both static and dynamic analysis are considered without losing essential information. In the first stage the number of concepts is reduced by merging similar concepts into clusters, while in the second stage the transformation function parameters of concepts are optimized. In order to show the benefit of using the proposed reduction approach, two sets of studies are conducted. First, a huge set of synthetic FCMs are generated, and the results of these statistical analyses are presented via various tables and figures. Subsequently, suggestions to the decision makers are given. Second, experimental studies are also presented to show the decision parameters and procedure for the proposed approach. The results show that after using the concept reduction approach presented in this study, the interpretability of FCM increases with an acceptable amount of information loss in the output concepts.

Original languageEnglish
JournalIEEE Transactions on Fuzzy Systems
Publication statusAccepted/In press - Jan 15 2018


  • Analytical models
  • Big Bang-Big Crunch Optimization
  • Clustering
  • Cognition
  • Concept Reduction
  • Decision making
  • Fuzzy cognitive maps
  • Fuzzy Cognitive Maps
  • Merging
  • Reduced order systems
  • Unsupervised and supervised reduction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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