A fuzzy classifier with ellipsoidal regions

Author

Abe, Shigeo ; Thawonmas, Ruck

Author_Institution

Dept. of Electr. & Electron. Eng., Kobe Univ., Japan

Volume

5

Issue

3

fYear

1997

fDate

8/1/1997 12:00:00 AM

Firstpage

358

Lastpage

368

Abstract

In this paper, we discuss a fuzzy classifier with ellipsoidal regions which has a learning capability. First, we divide the training data for each class into several clusters. Then, for each cluster, we define a fuzzy rule with an ellipsoidal region around a cluster center. Using the training data for each cluster, we calculate the center and the covariance matrix of the ellipsoidal region for the cluster. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. We evaluate our method using the Fisher iris data, numeral data of vehicle license plates, thyroid data, and blood cell data. The recognition rates (except for the thyroid data) of our classifier are comparable to the maximum recognition rates of the multilayered neural network classifier and the training times (except for the iris data) are two to three orders of magnitude shorter

Keywords

covariance analysis; fuzzy set theory; learning (artificial intelligence); pattern classification; Fisher iris data; blood cell data; clusters; covariance matrix; ellipsoidal regions; fuzzy classifier; learning capability; maximum recognition rates; membership functions; multilayered neural network classifier; thyroid data; training times; vehicle license plates; Blood; Cells (biology); Covariance matrix; Input variables; Iris; Licenses; Neural networks; Testing; Training data; Upper bound;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.618273

Filename

618273