Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm

Authors:
Shinn-Ying Ho;Chia-Cheng Liu;Soundy Liu
Affiliations:
Department of Information Engineering, Feng Chia University, 100 Wenhwa Road, Seatwen, Taichung 407, Taiwan;Department of Information Engineering, Feng Chia University, 100 Wenhwa Road, Seatwen, Taichung 407, Taiwan;Department of Information Engineering, Feng Chia University, 100 Wenhwa Road, Seatwen, Taichung 407, Taiwan
Venue:
Pattern Recognition Letters
Year:
2002

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Abstract

The goal of designing an optimal nearest neighbor classifier is to maximize the classification accuracy while minimizing the sizes of both the reference and feature sets. A novel intelligent genetic algorithm (IGA) superior to conventional GAs in solving large parameter optimization problems is used to effectively achieve this goal. It is shown empirically that the IGA-designed classifier outperforms existing GA-based and non-GA-based classifiers in terms of classification accuracy and total number of parameters of the reduced sets.