文本分类中的类别信息特征选择方法

随着网上电子文档的急剧增长，文本分类技术在信息检索中的应用变得日益重要.特征维数增加会使样本统计特性的评估变得更加困难，从而降低分类器的泛化能力，出现“过学习”的现象.因此，文档特征的选择和提取是文本分类的必要前提.提出一种基于类别信息的特征选择方法，该方法在尽量保留文档信息的同时，考虑了文档的类别信息.实验表明，这种方法的分类性能比较好，特别是在微平均指标上，与OCFS以及卡方统计量相比有较大幅度的提高.

Class information feature selection method for text classification

Lack of standard Chinese mail dataset,the performance of various Spam-filter systems can't be evaluated.The further research on the issue concerning the standard sample generation is made,through analyzing the problems on the collection of email samples.Meanwhile,the design of a standard sample generation system applied in real environment is given.A standard email dataset for evaluating the email filter system is provided,and will be finally developed to be a base corpus of email filtering technique.