Improving Michigan-style fuzzy-rule base classification generation using a Choquet-like Copula-based aggregation function

Edward Hinojosa-Cardenas, Edgar Sarmiento-Calisaya, Heloisa A. Camargo, Jose Antonio Sanz

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Resumen

This paper presents a modification of a Michigan-style fuzzy rule based classifier by applying the Choquet-like Copula-based aggregation function, which is based on the minimum t-norm and satisfies all the conditions required for an aggregation function. The proposed new version of the algorithm aims at improving the accuracy in comparison to the original algorithm and involves two main modifications: replacing the fuzzy reasoning method of the winning rule by the one based on Choquet-like Copula-based aggregation function and changing the calculus of the fitness of each fuzzy rule. The modification proposed, as well as the original algorithm, uses a (1+1) evolutionary strategy for learning the fuzzy rule base and it shows promising results in terms of accuracy, compared to the original algorithm, over ten classification datasets with different sizes and different numbers of variables and classes.

Idioma originalInglés
PublicaciónCEUR Workshop Proceedings
Volumen3074
EstadoPublicada - 2021
Evento13th International Workshop on Fuzzy Logic and Applications, WILF 2021 - Vietri sul Mare, Italia
Duración: 20 dic. 202122 dic. 2021

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© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

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