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JACIII Vol.1 No.1 pp. 31-36
doi: 10.20965/jaciii.1997.p0031
(1997)

Paper:

An Efficient Method for Extracting Fuzzy Classification Rules from High Dimensional Data

Stephen L. Chin

Rockwell Science Center, 1049 Camino Dos Rios Thousand Oaks, California 91360, USA

Received:
March 30, 1997
Accepted:
May 20, 1997
Published:
October 20, 1997
Keywords:
Clustering, Classification, Fuzzy rule learning, Feature selection
Abstract

We present an efficient method for extracting fuzzy classification rules from high dimensional data. A cluster estimation method called subtractive clustering is used to efficiently extract rules from a high dimensional feature space. A complementary search method can quickly identify the important input features from the resultant high dimensional fuzzy classifier, and thus provides the ability to quickly generate a simpler, more robust fuzzy classifier that uses a minimal number of input features. These methods are illustrated through the benchmark iris data and through two aerospace applications.

Cite this article as:
Stephen L. Chin, “An Efficient Method for Extracting Fuzzy Classification Rules from High Dimensional Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.1, No.1, pp. 31-36, 1997.
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