A More Accurate Text Classifier for Positive and Unlabeled data

摘要

Almost all LPU algorithms rely heavily on two steps: exploiting reliable negative dataset and supplementing positive dataset. For above two steps, this paper originally proposes a two-step approach, that is, CoTrain-Active. The first step, employing CoTrain algorithm, iterates to purify the unlabeled set with two individual SVM base classifiers. The second step, adopting active-learning algorithm, further expands the positive set effectively by request the true label for the "suspect positive" examples. Comprehensive experiments demonstrate that our approach is superior to Biased-SVM which is said to be previous best. Moreover, CoTrain-Active is especially suitable for those situations where the given positive dataset P is extremely insufficient.