T-Test feature selection approach based on term frequency for text categorization

Feature selection techniques play an important role in text categorization (TC), especially for the large-scale TC tasks. Many new and improved methods have been proposed, and most of them are based on document frequency, such as the famous Chi-square statistic and information gain etc. These methods based on document frequency, however, have two shortcomings: (1) they are not reliable for low-frequency terms, that is, low-frequency terms will be filtered because of their smaller weights; and (2) they only count whether one term occurs within a document and ignore term frequency. Actually, high-frequency term (except stop words) occurred in few documents is often regards as a discriminators in the real-life corpus. Aimed at solving the above drawbacks, the paper focuses on how to construct a feature selection function based on term frequency, and proposes a new approach using student t-test. The t-test function is used to measure the diversity of the distributions of a term frequency between the specific category and the entire corpus. Extensive comparative experiments on two text corpora using three classifiers show that the proposed approach is comparable to the state-of-the-art feature selection methods in terms of macro-^F1 and micro-^F1. Especially on micro-^F1, our method achieves slightly better performance on Reuters with kNN and SVMs classifiers, compared to ^χ2, and IG.