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A Survey of Recent Trends in One Class Classification

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Book cover Artificial Intelligence and Cognitive Science (AICS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6206))

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Abstract

The One Class Classification (OCC) problem is different from the conventional binary/multi-class classification problem in the sense that in OCC, the negative class is either not present or not properly sampled. The problem of classifying positive (or target) cases in the absence of appropriately-characterized negative cases (or outliers) has gained increasing attention in recent years. Researchers have addressed the task of OCC by using different methodologies in a variety of application domains. In this paper we formulate a taxonomy with three main categories based on the way OCC has been envisaged, implemented and applied by various researchers in different application domains. We also present a survey of current state-of-the-art OCC algorithms, their importance, applications and limitations.

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Authors and Affiliations

  1. College of Engineering and Informatics, National University of Ireland, Galway, Ireland

    Shehroz S. Khan & Michael G. Madden

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  1. Shehroz S. Khan
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  2. Michael G. Madden
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Editors and Affiliations

  1. Lero, International Science Centre, University of Limerick, Limerick, Ireland

    Lorcan Coyle

  2. CSIRO Tasmanian ICT centre, GPO Box 1538, 7001, Hobart, Tasmania, Australia

    Jill Freyne

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Khan, S.S., Madden, M.G. (2010). A Survey of Recent Trends in One Class Classification. In: Coyle, L., Freyne, J. (eds) Artificial Intelligence and Cognitive Science. AICS 2009. Lecture Notes in Computer Science(), vol 6206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17080-5_21

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  • DOI: https://doi.org/10.1007/978-3-642-17080-5_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17079-9

  • Online ISBN: 978-3-642-17080-5

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