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2014 Volume 29
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RESEARCH ARTICLE   Open Access    

One-class classification: taxonomy of study and review of techniques

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  • Abstract: 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.
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  • Cite this article

    Shehroz S. Khan, Michael G. Madden. 2014. One-class classification: taxonomy of study and review of techniques. The Knowledge Engineering Review 29(3)345−374, doi: 10.1017/S026988891300043X
    Shehroz S. Khan, Michael G. Madden. 2014. One-class classification: taxonomy of study and review of techniques. The Knowledge Engineering Review 29(3)345−374, doi: 10.1017/S026988891300043X

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One-class classification: taxonomy of study and review of techniques

The Knowledge Engineering Review  29 2014, 29(3): 345−374  |  Cite this article

Abstract: Abstract: 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.

    • The authors are grateful to Dr D.M.J. Tax, Dr L. Manevitz, Dr K. Li, Dr H. Yu and Dr T. Onoda for their kind permission to reproduce figures from their respective papers.

    • Readers are advised to refer to detailed literature survey on outlier detection by Chandola et al. (2009).

    • Readers are advised to refer to detailed literature survey on novelty detection by Markou and Singh (2003a, 2003b).

    • http://www.daviddlewis.com/resources/testcollections/reuters21578/ (Accessed January 2012).

    • ftp://ftp.ics.uci.edu/pub/machine-learning-databases/mfeat/ (Accessed August 2013).

    • http://lib.stat.cmu.edu/datasets/ (Accessed January 2012).

    • Readers are advised to refer to survey paper by Zhang and Zuo (2008)

    • Readers are advised to refer to survey paper on semi-supervised learning by Zhu (2005)

    • http://www.cs.uic.edu/liub/LPU/LPU-download.html [Accessed: Jan-2012]

    • http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/ [Accessed: Jan-2012]

    • http://www.csie.ntu.edu.tw/cjlin/libsvmtools/datasets/ (Accessed July 2013).

    • Copyright © Cambridge University Press 2014 2014Cambridge University Press
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    Cite this article
    Shehroz S. Khan, Michael G. Madden. 2014. One-class classification: taxonomy of study and review of techniques. The Knowledge Engineering Review 29(3)345−374, doi: 10.1017/S026988891300043X
    Shehroz S. Khan, Michael G. Madden. 2014. One-class classification: taxonomy of study and review of techniques. The Knowledge Engineering Review 29(3)345−374, doi: 10.1017/S026988891300043X
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