| Abstract |
Although many more complex learning algorithms exist, k-nearest neighbor is still one of the most successful classifiers in real-world applications. One of the ways of scaling up the k-nearest neighbors classifier to deal with large datasets is instance selection. Due to the constantly growing amount of data in almost any pattern recognition task, we need more efficient instance selection algorithms, which must achieve larger reductions while maintaining the accuracy of the selected subset.\n In this paper we present a way to improve instance selection by allowing the algorithms to select instances more than once. In this way, fewer instances can cover more portions of the space, and the same testing accuracy of the standard approach can be obtained faster and with fewer instances. Although the approach is general enough to be used in any instance selection algorithm, we focus on evolutionary instance selection due to its superior performance.\n An extensive comparison using 45 datasets from the UCI Machine Learning Repository shows the usefulness of our approach compared with the established method of evolutionary instance selection. Our method is able to, in the worst case, match the accuracy obtained by standard instance selection with a larger reduction and shorter execution time. In addition, the method is applied to class-imbalance problems with very good results. (c) 2012 Elsevier B.V. All rights reserved. |
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