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A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems
Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)
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In this contribution, we propose a new method to automatically learn the knowledge base of a Fuzzy Rule-Based Classification System (FRBCS) by selecting an adequate set of features and by finding an appropiate granularity for them. This process uses a multiobjective genetic algorithm and considers a simple generation method to derive the fuzzy classification rules. 0-7803-7@78-3/0U$l0~00 (C)u#)l WE.
doi:10.1109/nafips.2001.943727
fatcat:squwgjiidjentgsapjgadeekym