One Class Text Classification using an Ensemble of Classifiers

Abstract

Traditional classification algorithms work in a closed-world scenario where the training data contains all existing classes. In contrast, open set classifiers can handle new input that does not belong to any of the classes seen during training. Open set classification has been studied intensively in the computer vision domain, primarily in handwriting recognition, face recognition, object classification and computer forensics. Here we are interested in open set classification in natural language processing in one class document classification. We propose a new system based on autoencoder for one class classification of documents leveraging the full text. Extending further, we propose a novel ensemble based classifier model, a combination of several basic classifiers, to detect if an incoming document belongs to the class known from training or an unknown class. We compare and evaluate our methods on existing one class classification datasets for NLP - 20 Newsgroups, reuters and webkb. We also extract and use a new full-text dataset from arxiv.org. Our methods significantly outperforms the current state-of-the-art approaches for one class document classification.