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http://dx.doi.org/10.5351/CKSS.2010.17.1.017

Optimal k-Nearest Neighborhood Classifier Using Genetic Algorithm  

Park, Chong-Sun (Department of Statistics, Sungkyunkwan University)
Huh, Kyun (Department of Statistics, Sungkyunkwan University)
Publication Information
Communications for Statistical Applications and Methods / v.17, no.1, 2010 , pp. 17-27 More about this Journal
Abstract
Feature selection and feature weighting are useful techniques for improving the classification accuracy of k-Nearest Neighbor (k-NN) classifier. The main propose of feature selection and feature weighting is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. In this paper, a novel hybrid approach is proposed for simultaneous feature selection, feature weighting and choice of k in k-NN classifier based on Genetic Algorithm. The results have indicated that the proposed algorithm is quite comparable with and superior to existing classifiers with or without feature selection and feature weighting capability.
Keywords
k-Nearest Neighborhood classifier; genetic algorithm; feature selection; feature weighting;
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