关键词: 文本分类, k-近邻, 快速分类, 样本裁剪, 样本补充

Abstract: The density of training data directly influences the efficiency and precision of k- Nearest Neighbor (kNN) text classifier. Two disadvantages had been uncovered by the analysis of density-based method while reducing the amount of training data in kNN text classification. One is that after being reduced, the even density of the training data is just based on the spherical region which has a radius of ε，rather than the equal distance of every training text. The other is that there is no treatment of the low-density training texts while plenty of low-density texts still exist in the training data after being reduced. An improved approach to the mentioned deficiencies was proposed: the reduction strategy was optimized to make the training data yield evenly and the appropriate data were supplemented into the low-density texts. It is shown that the improved method has a distinctly better performance on both algorithm stability and accuracy.

Key words: text categorization, k- Nearest Neighbor (kNN), fast classification, sample reduction, sample supplement