Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns

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dc.contributor.author Zhao, Yanchang en_US
dc.contributor.author Zhang, Huaifeng en_US
dc.contributor.author Wu, Shan Shan en_US
dc.contributor.author Pei, Jian en_US
dc.contributor.author Cao, Longbing en_US
dc.contributor.author Zhang, Chengqi en_US
dc.contributor.author Bohlscheid, Hans-Michael en_US
dc.contributor.editor Wray L. Buntine; Marko Grobelnik; Dunja Mladenic; John Shawe-Taylor en_US
dc.date.accessioned 2010-06-16T05:00:04Z
dc.date.available 2010-06-16T05:00:04Z
dc.date.issued 2009 en_US
dc.identifier 2009001109 en_US
dc.identifier.citation Zhao Yanchang et al. 2009, 'Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns', Springer, Berlin / Heidelberg, pp. 648-663. en_US
dc.identifier.issn 978-3-642-04173-0 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/11925
dc.description.abstract Debt detection is important for improving payment accuracy in social security. Since debt detection from customer transactional data can be generally modelled as a fraud detection problem, a straightforward solution is to extract features from transaction sequences and build a sequence classifier for debts. The existing sequence classification methods based on sequential patterns consider only positive patterns. However, according to our experience in a large social security application, negative patterns are very useful in accurate debt detection. In this paper, we present a successful case study of debt detection in a large social security application. The central technique is building sequence classification using both positive and negative sequential patterns. en_US
dc.language en_US
dc.publisher Springer en_US
dc.relation.isbasedon http://dx.doi.org/10.1007/978-3-642-04174-7_42 en_US
dc.title Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns en_US
dc.parent Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009 en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Berlin / Heidelberg en_US
dc.identifier.startpage 648 en_US
dc.identifier.endpage 663 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 998488 en_US
dc.personcode 995032 en_US
dc.personcode 10793750 en_US
dc.personcode 0000058657 en_US
dc.personcode 034535 en_US
dc.personcode 011221 en_US
dc.personcode 00102347 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom European Conference on Machine Learning en_US
dc.date.activity 20090907 en_US
dc.location.activity Bled, Slovenia en_US
dc.description.keywords sequence classification, negative sequential patterns en_US


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