A Robust Discriminative Term Weighting Based Linear Discriminant Method for Text Classification

Text classification is widely used in applications ranging from e-mail filtering to review classification. Many of these applications demand that the classification method be efficient and robust, yet produce accurate categorizations by using the terms in the documents only. We present a supervised text classification method based on discriminative term weighting, discrimination information pooling, and linear discrimination. Terms in the documents are assigned weights according to the discrimination information they provide for one category over the others. These weights also serve to partition the terms into two sets. A linear opinion pool is adopted for combining the discrimination information provided by each set of terms yielding a two-dimensional feature space. Subsequently, a linear discriminant function is learned to categorize the documents in the feature space. We provide intuitive and empirical evidence of the robustness of our method with three term weighting strategies. Experimental results are presented for data sets from three different application areas. The results show that our method's accuracy is higher than other popular methods, especially when there is a distribution shift from training to testing sets. Moreover, our method is simple yet robust to different application domains and small training set sizes.