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A New Method to Scale Up Effect of K-Nearest-Neighbor

RU Qiang-xi1, LIU Yong2 (1.Department of Mathematics and Physics Teaching,Luoyang Institute of Science and Technology, Luoyang 471023, China;2.Department of Mathematics and Physics Teaching,Luoyang Institute of Science and Technology, Luoyang 471023, China)  
The k-Nearest-Neighbor (KNN) algorithm has been widely used in data mining areas. But, When the samples become more and more large and characteristic attributes become more and more numerous, then KNN algorithm becomes much lower. A improved KNN algorithm PRMKNN is proposed in the paper ,which first applies Principle Component Analysis(PCA)and rough set theory(RS) to realize feature extraction, We use PCA on selecting the input vector,and use RS on reducing the inessential factors for classification ,then simulation annealing algorithm is used to generate random subset of attributes, and with the simple voting method, the outputs of the multiple KNN classifiers are combined. The method can improve the classification precision and efficiency effectively.
【CateGory Index】: TP311.13
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