Optimal reference subset selection for nearest neighbor classification by tabu search

This paper presents an approach to select the optimal reference subset (ORS) for nearest neighbor classi$er. The optimal reference subset, which has minimum sample size and satis$es a certain resubstitution error rate threshold, is obtained through a tabu search (TS) algorithm. When the error rate threshold is set to zero, the algorithm obtains a near minimal consistent subset of a given training set. While the threshold is set to a small appropriate value, the obtained reference subset may have reasonably good generalization capacity. A neighborhood exploration method and an aspiration criterion are proposed to improve the e4ciency of TS. Experimental results based on a number of typical data sets are presented and analyzed to illustrate the bene$ts of the proposed method. The performances of the result consistent and non-consistent reference subsets are evaluated.? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.