Research on feature dimension reduction in text classification
文本分类中的特征降维方法研究

Feature dimension reduction is an important part of the procedure of text categorization, in order to improve the accuracy of feature dimension reduction, select the words that can distinguish categories effectively, and ultimately improve the effect of text classification, this paper proposed a new approach for feature selection by comprehensively taking account of text concentration among classes, dispersion within the text classes and word frequency concentration among classes. While getting overall assessment of the word in text set, it proposed new function of overall assessment by using the final assessment value, which was the difference of the maximum and the second largest value. The test compared this method with the traditional feature dimension method, results indicate better effect in Chinese text categorization.