关键词: 分类, KNN算法, 属性值, 信息熵

Abstract: In order to improve traditional KNN and KNN with weighted distance，which is on the distance definition and test mode，an improved algorithm entropy-KNN based on the classification importance of an attribute value is proposed.At first，a distance of the two samples is defined as the average information entropy of the same attribute values.The distance can effectively measure the similarity degree of the two samples.Secondly，the Entropy-KNN selects the K nearest neighbors by the distance above.Finally，the class label of the test sample is decided by the average distance and the numbers on the respective class.The experimental results on mushroom data set show this approach has much better than traditional KNN and KNN with weighted distance.

Key words: classification, K-nearest neighbor algorithm, attribute value, information entropy