A novel feature weight algorithm for text categorization
Wenqian Shang, Hongbin Dong, Haibin Zhu, Yongbin Wang · 2008
With the development of the Web, large numbers of documents are put onto the Internet. More and more digital libraries, news sources and inner data of companies are available. Automatic text categorization becomes more and more important for dealing with massive data. However, text preprocessing is still the bottleneck of text categorization based on vector space model (VSM). The result of text preprocessing directly affects the performance and precision of categorization. Moreover, feature selection and feature weight become the major obstacles of text preprocessing. In this paper, we mainly focus on feature weight. We present a novel feature weight algorithm----TF-Gini that can improve the categorization performance significantly. The experiment results verify the effectiveness of this algorithm.