Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to?
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IEEE Computational Intelligence Magazine
Date
2019-01Referencia bibliográfica
A. Fernandez, F. Herrera, O. Cordon, M. Jose del Jesus and F. Marcelloni, "Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to?," in IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 69-81, Feb. 2019, doi: 10.1109/MCI.2018.2881645. keywords: {Fuzzy systems;Data models;Data science;Fuzzy sets;Computational modeling;Task analysis;Genetic algorithms;Zadeh, Lotfi},
Abstract
Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysis of the evolutionary fuzzy systems research field. To this end, the "4 W" questions are posed and addressed with the aim of understanding the current context of this topic and its significance. Specifically, it will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area. They must play an important role for the emerging area of eXplainable Artificial Intelligence (XAI) learning from data.