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dc.contributor.authorKhan, Shehroz S.en
dc.contributor.authorMadden, Michael G.en
dc.date.accessioned2010-12-03T10:35:53Zen
dc.date.available2010-12-03T10:35:53Zen
dc.date.issued2009-08en
dc.identifier.urihttp://hdl.handle.net/10379/1472en
dc.description.abstractThe 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.formatapplication/pdfen
dc.languageenen
dc.language.isoenen
dc.publisherSpringer Verlag - LNAIen
dc.relationOne Class Classification, Surveyen
dc.relation.ispartofseriesVolume 6206;181-190,en
dc.subjectOne Class Classificationen
dc.subjectOutlier Detectionen
dc.subjectSupport Vector Machinesen
dc.subjectPositive and Unlabeled Dataen
dc.subject.lcshComputer Science, Machine Learningen
dc.titleA Survey of Recent Trends in One Class Classi cationen
dc.typeConference Paperen
dc.description.peer-reviewedpeer-revieweden
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