Positive Unlabeled Learning Algorithm for One Class Classification of Social Text Stream with only very few Positive Training Samples

Text classification using a small labelled set (positive data set) and large unlabeled data is seen as a promising technique especially in case of text stream classification where it is highly possible that only few positive data and no negative data is available. This paper studies how to devise a positive and unlabeled learning technique for the text stream environment. Our proposed approach works in two steps. Firstly we use the PNLH (Positive example and negative example labelling heuristic) approach for extracting both positive and negative example from unlabeled data. This extraction would enable us to obtain an enriched vector representation for the new test messages. Secondly we construct a one class classifier by using one class SVM classifier. Using the enriched vector representation as the input in one class SVM classifier predicts the importance level of each text message. Keywords: Positive and unlabeled learning, one class SVM (Support Vector Machine), one class classification, text stream classification

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure