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dc.contributor.authorOosterlinck, Dries
dc.contributor.authorBenoit, Dries F.
dc.contributor.authorBaecke, Philippe
dc.date.accessioned2019-12-05T09:12:17Z
dc.date.available2019-12-05T09:12:17Z
dc.date.issued2020en_US
dc.identifier.issn0377-2217
dc.identifier.doi10.1016/j.ejor.2019.10.015
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6423
dc.description.abstractOne-class classification is the standard procedure for novelty detection. Novelty detection aims to identify observations that deviate from a determined normal behaviour. Only instances of one class are known, whereas so called novelties are unlabelled. Traditional novelty detection applies methods from the field of outlier detection. These standard one-class classification approaches have limited performance in many real business cases. The traditional techniques are mainly developed for industrial problems such as machine condition monitoring. When applying these to human behaviour, the performance drops significantly. This paper proposes a method that improves existing approaches by creating semi-synthetic novelties in order to have labelled data for the two classes. Expert knowledge is incorporated in the initial phase of this data generation process. The method was deployed on a real-life test case where the goal was to detect fraudulent subscriptions to a telecom family plan. This research demonstrates that the two-class expert model outperforms a one-class model on the semi-synthetic dataset. In a next step the model was validated on a real dataset. A fraud detection team of the company manually checked the top predicted novelties. The results show that incorporating expert knowledge to transform a one-class problem into a two-class problem is a valuable method.en_US
dc.language.isoenen_US
dc.publisherScienceDirecten_US
dc.subjectAnalyticsen_US
dc.subjectOne-class classificationen_US
dc.subjectNovelty detectionen_US
dc.subjectExpert Knowledgeen_US
dc.subjectDecision Support Systemsen_US
dc.titleFrom one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviouren_US
dc.identifier.journalEuropean Journal of Operational Researchen_US
dc.source.volume282
dc.source.issue3
dc.source.beginpage1011
dc.source.endpage1024
dc.contributor.departmentFaculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, Ghent B-9000, Belgiumen_US
vlerick.knowledgedomainMarketing & Salesen_US
vlerick.typearticleVlerick strategic journal article
vlerick.vlerickdepartmentMKTen_US
dc.identifier.vperid151145en_US


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