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A Survey of Recent Trends in One Class Classi cation

ARAN - Access to Research at NUI Galway

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dc.contributor.author Khan, Shehroz S. en
dc.contributor.author Madden, Michael G. en
dc.date.accessioned 2010-12-03T10:35:53Z en
dc.date.available 2010-12-03T10:35:53Z en
dc.date.issued 2009-08 en
dc.identifier.uri http://hdl.handle.net/10379/1472 en
dc.description.abstract The One Class Classification (OCC) problem is di fferent from the conventional binary/multi-class classi fication 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 diff erent 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. en
dc.format application/pdf en
dc.language en en
dc.language.iso en en
dc.publisher Springer Verlag - LNAI en
dc.relation One Class Classification, Survey en
dc.relation.ispartofseries Volume 6206;181-190, en
dc.subject One Class Classification en
dc.subject Outlier Detection en
dc.subject Support Vector Machines en
dc.subject Positive and Unlabeled Data en
dc.subject.lcsh Computer Science, Machine Learning en
dc.title A Survey of Recent Trends in One Class Classi cation en
dc.type Conference Paper en
dc.description.peer-reviewed peer-reviewed en

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