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Erschienen in:
2003 | OriginalPaper | Buchkapitel
A Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems
verfasst von : Oscar Cordón, María José Del Jesus, Francisco Herrera, Luis Magdalena, Pedro Villar
Erschienen in: Interpretability Issues in Fuzzy Modeling
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.