Title :
A study on generating fuzzy classification rules using histograms
Author :
Ishibuchi, Hisao ; Nakashima, Tomoharu
Author_Institution :
Dept. of Ind. Eng., Osaka Prefecture Univ., Japan
Abstract :
We examine the performance of four approaches to the fuzzy rule generation for pattern classification problems. Two approaches generate a single fuzzy if-then rule for each class by specifying the membership function of each antecedent fuzzy set using the information about attribute values of training patterns. The other two approaches are based on fuzzy grids with homogeneous fuzzy partitions of each attribute. Since these four approaches are very simple and involve no time-consuming procedures, they can be easily implemented and applied to real-world pattern classification problems. The performance of each approach for test patterns (i.e., the generalization of ability of each approach) is evaluated by cross-validation techniques on commonly used data sets. Simulation results are compared with the performance of various classification methods reported in the literature
Keywords :
fuzzy logic; fuzzy set theory; knowledge based systems; learning (artificial intelligence); pattern classification; fuzzy classification; fuzzy rule generation; fuzzy set theory; histograms; learning; membership function; pattern classification; Computer simulation; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Histograms; Industrial engineering; Knowledge based systems; Neural networks; Pattern classification; Testing;
Conference_Titel :
Knowledge-Based Intelligent Electronic Systems, 1998. Proceedings KES '98. 1998 Second International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-4316-6
DOI :
10.1109/KES.1998.725837