Text classification without labeled negative documents

This paper presents a new solution for the problem of building a text classifier with a small yet of labeled positive documents (P) and a large set of unlabeled documents (U). Here, the unlabeled documents are mixed with both of the positive and negative documents. In other words, no document is labeled as negative. This makes the task of building a reliable text classifier challenging. In general, the existing approaches for solving this kind of problem use a two-step approach: i) extract the negative documents (N) from U; and ii) build a classifier based on P and N. However, none of the reported studies tries to further extract any positive documents (P') from U. Intuitively, extracting P' from U will increase the reliability of the classifier. However, extracting P' from U is difficult. A document in U that possesses some of the features exhibited in P does not necessarily mean that it is a positive document, and vice versa. It is very sensitive to extract positive documents, because those extracted positive samples may become noises. The very large size of U and the very high diversity exhibited there also contribute to the difficulty of extracting any positive documents. In this paper, we propose a partition-based heuristic which aims at extracting both of the positive and negative documents in U. Extensive experiments based on three benchmarks are conducted. The favorable results indicated that our proposed heuristic outperforms all of the existing approaches significantly, especially in the case where the size of P is extremely small.