Prof. Vanhoof Koen

FCM-based classifiers

Fuzzy Cognitive Maps (FCMs) have proven to be a suitable methodology for the design of knowledge-based systems. By combining both uncertainty description and cognitive mapping, this technique represents the knowledge of systems that are characterized by ambiguity and complexity. Actually, FCMs can be designed as recurrent neural networks that include some elements of fuzzy logic during the knowledge engineering phase. One of the most attractive features of FCMs relies on their network interpretability. While the FCM literature contains many studies claiming how this technique is able to model complex and dynamical systems, we explore another promising research field: the use of FCMs in solving pattern classification problems keeping the interpretability. In this invited talk, we revise the main advances in the area of FCM-based classifiers and open challenges to be confronted.