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JACIII Vol.4 No.2 pp. 138-145
doi: 10.20965/jaciii.2000.p0138
(2000)

Paper:

Pattern and Feature Selection by Genetic Algorithms in Nearest Neighbor Classification

Hisao Ishibuchi and Tomoharu Nakashima

Department of Industrial Engineering, Osaka Prefecture University Gakuen-cho 1-1, Sakai, Osaka 599-8531, Japan

Received:
March 15, 1999
Accepted:
July 20, 1999
Published:
March 20, 2000
Keywords:
Genetic algorithms, Pattern classification, Instance selection, Feature selection, Nearest neighbor
Abstract

This paper proposes a genetic-algorithm-based approach for finding a compact reference set in nearest neighbor classification. The reference set is designed by selecting a small number of reference patterns from a large number of training patterns using a genetic algorithm. The genetic algorithm also removes unnecessary features. The reference set in our nearest neighbor classification consists of selected patterns with selected features. A binary string is used for representing the inclusion (or exclusion) of each pattern and feature in the reference set. Our goal is to minimize the number of selected patterns, to minimize the number of selected features, and to maximize the classification performance of the reference set. Computer simulations on commonly used data sets examine the effectiveness of our approach.

Cite this article as:
Hisao Ishibuchi and Tomoharu Nakashima, “Pattern and Feature Selection by Genetic Algorithms in Nearest Neighbor Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.4, No.2, pp. 138-145, 2000.
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