Customer Activity Sequence Classification for Debt Prevention in Social Security

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Show simple item record Zhang, H Zhao, Y Cao, L Zhang, C Bohlscheid, H 2010-05-28T09:47:04Z 2009-01
dc.identifier.citation Journal Of Computer Science And Technology, 2009, 24 (6), pp. 1000 - 1009
dc.identifier.issn 1860-4749
dc.identifier.other C1 en_US
dc.description.abstract From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern selection and classifier building very time-consuming. The fact is that, in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which are the most strongly correlated to each target class. In this step, an aggressive strategy is employed to select a small set of sequential patterns. Secondly, pattern pruning and serial coverage test are done on the mined patterns. The patterns that pass the serial test are used to build the sub-classifier at the first level of the final classifier. And thirdly, the training samples that cannot be covered are fed back to the sequential pattern mining stage with updated parameters. This process continues until predefined interestingness measure thresholds are reached, or all samples are covered. The patterns generated in each loop form the sub-classifier at each level of the final classifier. Within this framework, the searching space can be reduced dramatically while a good classification performance is achieved. The proposed algorithm is tested in a real-world business application for debt prevention in social security area. The novel sequence classification algorithm shows the effectiveness and efficiency for predicting debt occurrences based on customer activity sequence data.
dc.publisher Springer
dc.relation.isbasedon 10.1007/s11390-009-9288-2
dc.title Customer Activity Sequence Classification for Debt Prevention in Social Security
dc.type Journal Article
dc.parent Journal Of Computer Science And Technology
dc.journal.volume 6
dc.journal.volume 24
dc.journal.number 6 en_US
dc.publocation Boston en_US
dc.identifier.startpage 1000 en_US
dc.identifier.endpage 1009 en_US FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 080110 Simulation and Modelling
dc.personcode 011221
dc.personcode 034535
dc.personcode 998488
dc.personcode 995032
dc.percentage 100 en_US Simulation and Modelling en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords sequential pattern mining - sequence classification - coverage test - interestingness measure 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 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)

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