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

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dc.contributor.author Zhao, Y
dc.contributor.author Zhang, H
dc.contributor.author Wu, S
dc.contributor.author Pei, J
dc.contributor.author Cao, L
dc.contributor.author Zhang, C
dc.contributor.author Bohlscheid, H
dc.contributor.editor Buntine, WL
dc.contributor.editor Grobelnik, M
dc.contributor.editor Mladenic, D
dc.contributor.editor Shawe-Taylor, J
dc.date.accessioned 2010-06-16T05:00:04Z
dc.date.issued 2009-01
dc.identifier.citation Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, 2009, pp. 648 - 663
dc.identifier.isbn 978-3-642-04173-0
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.
dc.publisher Springer
dc.relation.isbasedon 10.1007/978-3-642-04174-7_42
dc.title Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns
dc.type Conference Proceeding
dc.parent Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009
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.conference European Conference on Machine Learning
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 011221
dc.personcode 034535
dc.personcode 998488
dc.personcode 995032
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.date.activity 2009-09-07
dc.location.activity Bled, Slovenia en_US
dc.description.keywords sequence classification, negative sequential patterns en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10


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