A new PU learning algorithm for text classification

This paper studies the problem of building text classifiers using positive and unlabeled examples. The primary challenge of this problem as compared with classical text classification problem is that no labeled negative documents are available in the training example set. We call this problem PU-Oriented text Classification. Our text classifier adopts traditional two-step approach by making use of both positive and unlabeled examples. In the first step, we improved the 1-DNF algorithm by identifying much more reliable negative documents with very low error rate. In the second step, we build a set of classifiers by iteratively applying SVM algorithm on training data set, which is augmented during iteration. Different from previous PU-oriented text classification works, we adopt the weighted vote of all classifiers generated in the iteration steps to construct the final classifier instead of choosing one of the classifiers as the final classifier. Experimental results on the Reuter data set show that our method increases the performance (F1-measure) of classifier by 1.734 percent compared with PEBL.