Pattern and Feature Selection by Genetic Algorithms in Nearest Neighbor Classification

This paper proposes a genetic-algonthm-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, i he 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 ot selected patterns, to minimize the number of selected features, and to maximize the classification performance ot the reterence set. Computer simulations on commonly used data sets examine the effectiveness of our approach. © Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/).