Fast supervised feature extraction by term discrimination information pooling
Proceedings of the 20th ACM international conference on Information and knowledge management
SmartDispatch: enabling efficient ticket dispatch in an IT service environment
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering and understanding documents via discrimination information maximization
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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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.