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Customer Activity Sequence Classification for Debt Prevention in Social Security

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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.

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Authors and Affiliations

  1. Payment Reviews Branch, Business Integrity Division, Centrelink, Canberra, Australia

    Huaifeng Zhang (Member, IEEE) & Hans Bohlscheid

  2. Centre for Quantum Computation and Intelligent Systems (QCIS), University of Technology, Sydney, Australia

    Yanchang Zhao (Member, IEEE), Longbing Cao (Senior Member, IEEE) & Chengqi Zhang (Senior Member, IEEE)

Authors
  1. Huaifeng Zhang
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  2. Yanchang Zhao
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  3. Longbing Cao
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  4. Chengqi Zhang
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  5. Hans Bohlscheid
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Corresponding author

Correspondence to Huaifeng Zhang.

Additional information

This work is supported by Australian Research Council Linkage Project under Grant No. LP0775041 and the Early Career Researcher Grant under Grant No. 2007002448 from University of Technology, Sydney, Australia.

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Zhang, H., Zhao, Y., Cao, L. et al. Customer Activity Sequence Classification for Debt Prevention in Social Security. J. Comput. Sci. Technol. 24, 1000–1009 (2009). https://doi.org/10.1007/s11390-009-9288-2

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  • DOI: https://doi.org/10.1007/s11390-009-9288-2

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