期刊
PATTERN RECOGNITION
卷 35, 期 7, 页码 1481-1490出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0031-3203(01)00137-6
关键词
nearest neighbor classifications; tabu search; reference set; prototype selection
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. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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