Fuzzy Classifier Design using Modified Genetic Algorithm

DOI

10.2991/ijcis.2010.3.3.9How to use a DOI?

Keywords

Fuzzy Classifier, If-then-Rules, Membership function, Genetic Algorithm.

Abstract

Development of fuzzy if- then rules and formation of membership functions are the important consideration in designing a fuzzy classifier system. This paper presents a Modified Genetic Algorithm (ModGA) approach to obtain the optimal rule set and the membership function for a fuzzy classifier. In the genetic population, the membership functions are represented using real numbers and the rule set is represented by the binary string. A modified form of cross over and mutation operators are proposed to deal with the mixed string. The proposed genetic operators help to improve the convergence speed and quality of the solution. The performance of the proposed approach is demonstrated through development of fuzzy classifier for Iris, Wine and Tcpdump data. From the simulation study it is found that the proposed Modified Genetic Algorithm produces a fuzzy classifier which has minimum number of rules and whose classification accuracy is better than the results reported in the literature.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - P.Ganesh Kumar
PY  - 2010
DA  - 2010/09/01
TI  - Fuzzy Classifier Design using Modified Genetic Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 334
EP  - 342
VL  - 3
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2010.3.3.9
DO  - 10.2991/ijcis.2010.3.3.9
ID  - Kumar2010
ER  -