Fuzzy Cognitive Maps (FCMs) are a type of recurrent neural network that consists of neural concepts and causal relations. FCMs allow decision-makers to analyze the interdependencies and causal relationships between different variables in a system, and also allow researchers and practitioners to classify, simulate and predict.
FCMs are useful for building more interpretable models than most state-of-the-art models. But it is also a fact that the interpretability of FCMs causes, as a counterpart, relatively higher errors than other models. In this lecture recent improvements that increase the accuracy and keep the explain ability are shown and explained.
The work to present has been a collaboration with researchers from Hasselt University (Belgium), Tilburg university (Netherlands) and Universidad Central Marta
Abreu de Las Villas (Cuba).