中文摘要：

分析了文本分类过程中存在的混淆类现象，主要研究混淆类的判别技术，进而改善文本分类的性能．首先，提出了一种基于分类错误分布的混淆类识别技术，识别预定义类别中的混淆类集合．为了有效判别混淆类，提出了一种基于判别能力的特征选取技术，通过评价某一特征对类别之间的判别能力实现特征选取．最后，通过基于两阶段的分类器设计框架，将初始分类器和混淆类分类器进行集成，组合了两个阶段的分类结果作为最后输出．混淆类分类器的激活条件是：当测试文本被初始分类器标注为混淆类类别时，即采用混淆类分类器进行重新判别在比较实验中采用了Newsgroup和863中文评测语料，针对单标签、多类分类器．实验结果显示，该技术有效地改善了分类性能．

英文摘要：

This paper analyzes confusion class phenomena existing in text classification procedure, and studies further confusion class discrimination techniques to improve the performance of text classification. In this paper, firstly a technique for confusion class recognition based on classification error distribution is proposed to recognize confusion class sets existing in the pre-defined taxonomy. To effectively discriminate confusion classes, this paper proposes an approach to feature selection based on discrimination capability in the procedure of which each candidate feature＇s discrimination capability for class pair is evaluated. At last, two-stage classifiers are used to integrate baseline classifier and confusion class classifiers, and in which the two output results from two stages are combined into the final output results. The confusion class classifiers in the second stage could be activated only when the output class of the input text assigned by baseline classifier in the first stage belongs to confusion classes, then the confusion class classifiers are used to discriminate the testing text again. In the comparison experiments, Newsgroup and 863 Chinese evaluation data collection are used to evaluate the effectiveness of the techniques proposed in this paper, respectively. Experimental results show that the methods could improve significantly the performance for single-label and multi-class classifier （SMC）.