Abstract

This paper present a novel method based on multi-objective genetic algorithm to construct fuzzy classification system. First, in order to relieve the problem of curse of dimensionality, a multi-objective genetic algorithm is used to accomplish feature selection and fuzzy partition with maximum classification performance and minimum number of features and minimum number of fuzzy rules, thus an initial fuzzy system is obtained. Then, a genetic algorithm is employed to select significant fuzzy rules with two objectives to achieve a compact fuzzy system. In order to improve the classification performance of the compact fuzzy system, a constrained genetic algorithm is utilized to optimize the parameters of the compact fuzzy system. The proposed approach is applied to the Iris and Wine benchmark problems, and the results show its validity.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.