Sequential Reduction Algorithm for Nearest Neighbor Rule
An effective training set reduction is one of the main problems in constructing fast 1-NN classifiers. A reduced set should be significantly smaller and ought to result in a similar fraction of correct classifications as a complete training set. In this paper a sequential reduction algorithm for nearest neighbor rule is described. The proposed method is based on heuristic idea of sequential adding and eliminating samples. The performance of the described algorithm is evaluated and compared with three other well-known reduction algorithms based on heuristic ideas, on four real datasets extracted from images.
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