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When calculating the similarity between samples, the conventional KNN algorithm deemscach attribute equally important, and ignores the distinction of the attributes' significance. In order to address this issue, this paper employed the earth mover's distance to calculate the weight of each condition attribute. Firstly, this method divided two distributions according to the nearest neighbor relationship. Then, it designed an evaluation function based on earth mover's distance to gauge the inconsistency degree between the neighborhood of each sample with regard to each attribute and its equivalent refinement induced by the decision attribute. Last, it transformed the inconsistency degree to the significance of the corresponding attribute to implement an attribute weighted KNN. Through systematic experiments on several datasets, it verifies that the proposed method is insensitive to parameters and can significantly improve the classification performance of KNN, and outperforms some state-of the-art classification methods. The results show that this method can select more accurate nearest neighbor samples by attribute weighting, and can be widely used in methods base on nearest neighbor. [ABSTRACT FROM AUTHOR]

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