Title :
A fuzzy classifier system that generates fuzzy if-then rules for pattern classification problems
Author :
Ishibuchi, Hsao ; Nakashima, Tomoharu ; Murata, Tadahiko
Author_Institution :
Dept. of Ind. Eng., Osaka Prefecture Univ., Japan
fDate :
29 Nov-1 Dec 1995
Abstract :
We propose a fuzzy classifier system that can automatically generate fuzzy if-then rules from numerical data (i.e., from training patterns) for multi-dimensional pattern classification problems. Classifiers in our approach are fuzzy if-then rules such as “If x p1 is small and xp2 is large then classify xp as Class 2”. The proposed classifier system can find a compact rule set by attaching large fitness values to such fuzzy if-then rules that can correctly classify many training patterns. That is, only fuzzy if-then rules with large fitness values are selected to construct a compact fuzzy system with high classification performance
Keywords :
fuzzy set theory; fuzzy systems; genetic algorithms; pattern classification; classification performance; compact rule set; fitness values; fuzzy classifier system; fuzzy if-then rules; fuzzy system; genetic algorithms; multidimensional pattern classification; numerical data; pattern classification; training patterns; Automatic control; Control systems; Fuzzy control; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Genetic algorithms; Humans; Industrial engineering; Pattern classification;
Conference_Titel :
Evolutionary Computation, 1995., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2759-4
DOI :
10.1109/ICEC.1995.487481