Optimal reference subset selection for nearest neighbor classification by tabu search

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This paper presents an approach to select the optimal reference subset (ORS) for nearest neighbor classifier. The optimal reference subset, which has minimum sample size and satisfies 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 efficiency of TS. Experimental results based on a number of typical data sets are presented and analyzed to illustrate the benefits of the proposed method. The performances of the result consistent and non-consistent reference subsets are evaluated.