A Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems

TitleA Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems
Publication TypeBook Chapter
Year of Publication2003
AuthorsCordón, O., del Jesus M. J., Herrera F., Magdalena Luis, and Villar Pedro
EditorCasillas, Jorge, Cordón O., Herrera F., and Magdalena Luis
Book TitleInterpretability Issues in Fuzzy Modeling
Pagination79–99
PublisherSpringer Berlin Heidelberg
CityBerlin, Heidelberg
ISBN Number978-3-540-37057-4
Abstract

In this contribution, we propose a genetic process to select an appropiate set of features in a Fuzzy Rule-Based Classification System (FRBCS) and to automatically learn the whole Data Base definition using a non linear scaling function to adapt the fuzzy partition contexts and determining an appropiate granularity for each of them. An ad-hoc data covering learning method is considered to obtain the Rule Base. The method uses a multiobjective genetic algorithm in order to obtain a good trade-off between accuracy and interpretability.

URLhttps://doi.org/10.1007/978-3-540-37057-4_4
DOI10.1007/978-3-540-37057-4_4