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2020 | OriginalPaper | Chapter

Symbolic Explanation Module for Fuzzy Cognitive Map-Based Reasoning Models

Authors : Fabian Hoitsma, Andreas Knoben, Maikel Leon Espinosa, Gonzalo Nápoles

Published in: Artificial Intelligence XXXVII

Publisher: Springer International Publishing

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Abstract

In recent years, pattern classification has started to move from computing models with outstanding prediction rates to models able to reach a suitable trade-off between accuracy and interpretability. Fuzzy Cognitive Maps (FCMs) and their extensions are recurrent neural networks that have been partially exploited towards fulfilling such a goal. However, the interpretability of these neural systems has been confined to the fact that both neural concepts and weights have a well-defined meaning for the problem being modeled. This rather naive assumption oversimplifies the complexity behind an FCM-based classifier. In this paper, we propose a symbolic explanation module that allows extracting useful insights and patterns from a trained FCM-based classifier. The proposed explanation module is implemented in Prolog and can be seen as a reverse symbolic reasoning rule that infers the inputs to be provided to the model to obtain the desired output.

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Metadata
Title
Symbolic Explanation Module for Fuzzy Cognitive Map-Based Reasoning Models
Authors
Fabian Hoitsma
Andreas Knoben
Maikel Leon Espinosa
Gonzalo Nápoles
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-63799-6_2

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