One-Class Classification: Taxonomy of Study and Review of Techniques
This is an incremental review paper that organizes and synthesizes existing research on OCC for researchers and practitioners in fields like outlier detection and concept learning.
This paper tackles the problem of one-class classification (OCC), where only positive class data is available, by presenting a taxonomy based on training data, algorithms, and application domains, and provides a comprehensive literature review of OCC techniques, including their significance, limitations, and applications.
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.