Estimating a one-class naive Bayes text classifier

Abstract

Nowadays more and more information extraction projects need to classify large amounts of text data. The common way to classify text is to build a supervised classifier trained on human-labeled positive and negative examples. In many cases, however, it is easy to label positive examples, but hard to label negative examples. In this paper, we address the problem of building a one-class classifier when only the positive examples are labeled. Previous works on building one-class classifier mostly use positive examples and unlabeled data. In this paper, we show that a configurable one-class classifier such as one-class naive Bayes can be optimized by examining the clustering quality of the classification on target data. We propose to use existing and new quality scores for determining clustering quality of the classification. Experimental analysis with real-world data show that our approach generally achieves high classification accuracy, and in some cases improves the accuracy by more than 10% compared to state-of-art baselines.