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.

Typically, spam filters are built on the assumption that the characteristics of e-mails in the training set is identical to those in individual users' inboxes on which it will be applied. This assumption is oftentimes incorrect leading to poor performance of the filter. A personalized spam filter is built by taking into account the characteristics of e-mails in individual users' inboxes. We present an automatic approach for personalized spam filtering that does not require users' feedback. The proposed algorithm builds a statistical model of significant spam and non-spam words from the labeled training set and then updates it in multiple passes over the unlabeled individual user's inbox. The personalization of the model leads to improved filtering performance. We evaluate our algorithm on two publicly available datasets. The results show that our algorithm is robust and scalable, and a viable solution to the server-side personalized spam filtering problem. Moreover, it outperforms published results on one dataset and its performance is equivalent to the others on the second dataset.

The volume of spam e-mails has grown rapidly in the last two years resulting in increasing costs to users, network operators, and e-mail service providers (ESPs). E-mail users demand accurate spam filtering with minimum effort from their side. Since the distribution of spam and non-spam e-mails is often different for different users a single filter trained on a general corpus is not optimal for all users. The question asked by ESPs is: How do you build robust and scalable automatic personalized spam filters? We address this question by presenting PSSF, a novel statistical approach for personalized service-side spam filtering. PSSF builds a discriminative classifier from a statistical model of spam and non-spam e-mails. A classifier is first built on a general training corpus that is then adapted in one or more passes of soft labeling and classifier rebuilding over each user's unlabeled e-mails. The statistical model captures the distribution of tokens in spam and non-spam e-mails. This model is robust in the sense that its size can be reduced significantly without degrading filtering performance. We evaluate PSSF on two datasets. The results demonstrate the superior performance and scalability of PSSF in comparison with other published results on the same datasets.