Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Pattern Classification (2nd Edition)
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Pattern Recognition, Third Edition
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A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.