关键词: K近邻, 特征, 联合熵, 条件属性, 分类

Abstract: Poor generalization of feature parameters classification and large category computation reduce the classification performace of K-Nearest Neighbor (KNN). An improved KNN based on union entropy under the attribute reduction condition was proposed. Firstly, the size of classification impact of data feature was measured by calculating the union entropy of two feature parameters relative to any two condition attributes, and the intrinsic relation was established between classified features and the specific classification process. Then, the method which reduced condition attributes according feature union entropy set was given. The theoretical analysis and the simulation experiment show that compared with the classical KNN, the improved algorithm has better classification performance.

Key words: K-Nearest Neighbor (KNN), feature, union entropy, condition attribute, classify