This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. In this paper, we first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques. 1.

Traditionally, text classifiers are built from labeled training examples. Labeling is usually done manually by human experts (or the users), which is a labor intensive and time consuming process. In the past few years, researchers investigated various forms of semi-supervised learning to reduce the burden of manual labeling. In this paper, we propose a different approach. Instead of labeling a set of documents, the proposed method labels a set of representative words for each class. It then uses these words to extract a set of documents for each class from a set of unlabeled documents to form the initial training set. The EM algorithm is then applied to build the classifier. The key issue of the approach is how to obtain a set of representative words for each class. One way is to ask the user to provide them, which is difficult because the user usually can only give a few words (which are insufficient for accurate learning). We propose a method to solve the problem. It combines clustering and feature selection. The technique can effectively rank the words in the unlabeled set according to their importance. The user then selects/labels some words from the ranked list for each class. This process requires less effort than providing words with no help or manual labeling of documents. Our results show that the new method is highly effective and promising.