Author:
Khan Shehroz S.,Madden Michael G.
Abstract
AbstractOne-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.
Publisher
Cambridge University Press (CUP)
Subject
Artificial Intelligence,Software
Reference185 articles.
1. Zhou J. , Chan K. L. , Chong V. F. H. , Krishnan S. M. 2005. Extraction of brain tumor from MR images using one-class support vector machine. In Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, 1–4.
2. Zhang J. , Lu J. , Zhang G. 2011. Combining one class classification models for avian influenza outbreaks. In 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), 190–196, Paris. IEEE.
3. PEBL: web page classification without negative examples;Yu;IEEE Transactions on Knowledge and Data Engineering,2004
4. Single-Class Classification with Mapping Convergence
5. Yang L. , Madden M. G. 2007. One-class support vector machine calibration using particle swarm optimisation. In AICS 2007, Dublin.
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