关键词: K最邻近算法, 熵权, 属性约简, 分类

Abstract: Dimension disaster in the classified function will directly impact on the efficiency and the rate of accuracy of K-Nearest Neighbor algorithm（KNN），this paper combines information entropy theory used to reduce attributes and KNN algorithm，and also determines weight of characteristic attributes based on the degree of correlation between characteristic attributes and target attribute to establish the intrinsic relation between the degree of correlation and weight.The simulation experiment shows that compared with traditional KNN algorithm the improved KNN algorithm based on entropy method raises the rate of accuracy enormously in classification，meanwhile it also maintains efficiency of classification.

Key words: K-Nearest Neighbor（KNN） Algorithm, entropy weight, attributes reduction, classification