Multiobjective genetic optimization of fuzzy partitions and t-norm parameters in fuzzy classifiers

Edward Hinojosa Cardenas, Heloisa A. Carmago

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

This paper proposes the use of a multiobjective genetic algorithm to tune fuzzy partitions and t-norm parameters in Fuzzy Rule Based Classifications Systems (FRBCSs). We consider a rule base and a data base already defined and apply a multiobjective genetic algorithm to tune the database, and simultaneously search for the most appropriate t-norm to be used in the inference engine. The optimization process is designed to handle the trade-off between interpretability and accuracy. We present a comparative study which examines a number of t-norms and their influence in the quality of the non-dominated solutions found in the optimization process. The experiments showed that significant improvements can be made in the Pareto front when the most appropriate t-norm is optimized for a specific domain. The proposed algorithm is based on the well-known technique Strength Pareto Evolutionary Algorithm (SPEA2).

Idioma originalInglés
Título de la publicación alojadaProceedings - 2012 Brazilian Conference on Neural Networks, SBRN 2012
Páginas154-159
Número de páginas6
DOI
EstadoPublicada - 2012
Publicado de forma externa
Evento2012 Brazilian Conference on Neural Networks, SBRN 2012 - Curitiba, Parana, Brasil
Duración: 20 oct. 201225 oct. 2012

Serie de la publicación

NombreProceedings - Brazilian Symposium on Neural Networks, SBRN
ISSN (versión impresa)1522-4899

Conferencia

Conferencia2012 Brazilian Conference on Neural Networks, SBRN 2012
País/TerritorioBrasil
CiudadCuritiba, Parana
Período20/10/1225/10/12

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