Show simple item record  

dc.contributor.authorHempstalk, Kathryn
dc.contributor.authorFrank, Eibe
dc.contributor.authorWitten, Ian H.
dc.coverage.spatialConference held at Antwerp, Belgiumen_NZ
dc.date.accessioned2008-12-19T00:57:31Z
dc.date.available2008-12-19T00:57:31Z
dc.date.issued2008
dc.identifier.citationHempstalk, K., Frank, E. & Witten, I.H. (2008) One-Class Classification by Combining Density and Class Probability Estimation. In Proceedings of European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I (pp. 505-519). Berlin: Springeren
dc.identifier.urihttps://hdl.handle.net/10289/1728
dc.description.abstractOne-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution, with the induction of a standard model for class probability estimation. In this method, the reference distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class probability estimates obtained in this way to form an adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem, we show that the combined model, consisting of both a density estimator and a class probability estimator, can improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines.en
dc.language.isoen
dc.publisherSpringer, Berlinen
dc.relation.urihttp://www.springerlink.com/content/713g6um816w1v6t6/?p=28cb69d988bc4c3d974cfc464afcc4ef&pi=0en
dc.sourceEuropean Conference on Principles of Data Mining and Knowledge Discoveryen_NZ
dc.subjectcomputer scienceen
dc.subjectone-class classificationen
dc.subjectMachine learning
dc.titleOne-Class Classification by Combining Density and Class Probability Estimationen
dc.typeConference Contributionen
dc.identifier.doi10.1007/978-3-540-87479-9_51en
dc.relation.isPartOfProc European Conference on Machine Learning and Knowledge Discovery in Databases (LNAI 5211)en_NZ
pubs.begin-page505en_NZ
pubs.elements-id17864
pubs.end-page519en_NZ
pubs.finish-date2008-09-19en_NZ
pubs.issuePART 1en_NZ
pubs.start-date2008-09-15en_NZ
pubs.volume5211en_NZ


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record