A Text Feature Selection Algorithm Based on Improved TFIDF

In Chinese text categorization system, for most classifiers using vector space model (VSM), all attributes of documents construct a high dimensional feature space. And the high dimensionality of feature space is the bottleneck of categorization. TFIDF is a kind of common methods used to measure the terms in a document. The method is easy but it doesn´t consider the unbalance distribution of terms among classes. This paper analyzed the TFIDF feature selection algorithm deeply, and proposed a new TFIDF feature selection method based on Gini index theory. Experimental results show the method is valid in improving the accuracy of text categorization.