Abstract
Debt detection is important for improving payment accuracy in social security. Since debt detection from customer transaction 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. For long-running debt detections, the patterns in the transaction sequences may exhibit variation from time to time, which makes it imperative to adapt classification to the pattern variation. In this paper, we present a novel adaptive sequence classification framework for debt detection in a social security application. The central technique is to catch up with the pattern variation by boosting discriminative patterns and depressing less discriminative ones according to the latest sequence data.
This work was supported by the Australian Research Council (ARC) Linkage Project LP0775041 and Early Career Researcher Grant from University of Technology, Sydney, Australia.
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References
Centrelink. Centrelink annual report 2004-2005. Technical report, Centrelink, Australia (2005)
Lesh, N., Zaki, M.J., Ogihara, M.: Mining Features for Sequence Classification. In: Proc.of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, USA, August 1999, pp. 342–346 (1999)
Tseng, V.S.M., Lee, C.-H.: CBS: A new classification method by using sequential patterns. In: Proc. of SIAM International Conference on Data Mining (SDM 2005), pp. 596–600 (2005)
Exarchos, T.P., Tsipouras, M.G., Papaloukas, C., Fotiadis, D.I.: A two-stage methodology for sequence classification based on sequential pattern mining and optimization. Data and Knowledge Engineering 66(3), 467–487 (2008)
Cheng, H., Yan, X., Han, J., Hsu, C.-W.: Discriminative Frequent Pattern Analysis for Effective Classification. In: Proc. of IEEE International Conference on Data Engineering (ICDE 2007), April 2007, pp. 716–725 (2007)
Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery 15(1), 55–86 (2007)
Bonchi, F., Giannotti, F., Mainetto, G., Pedreschi, D.: A classification-based methodology for planning audit strategies in fraud detection. In: Proc. of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 1999, pp. 175–184 (1999)
Rosset, S., Murad, U., Neumann, E., Idan, Y., Pinkas, G.: Discovery of fraud rules for telecommunications - challenges and solutions. In: Proc. of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 1999, pp. 409–413 (1999)
Fawcett, T., Provost, F.: Adaptive fraud detection. Data Mining and Knowledge Discovery 1, 291–316 (1997)
Xu, J., Sung, A.H., Liu, Q.: Tree based behavior monitoring for adaptive fraud detection. In: Proc. of the 18th International Conference on Pattern Recognition, Washington, DC, USA, pp. 1208–1211 (2006)
Lu, F., Boritz, J.E., Covvey, D.: Adaptive fraud detection using Benfords Law. In: Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006. LNCS (LNAI), vol. 4013, pp. 347–358. Springer, Heidelberg (2006)
Lee, W., Stolfo, S.J., Mok, K.W.: Adaptive intrusion detection: a data mining approach. Artificial Intelligence Review 14, 533–567 (2000)
Zhang, H., Zhao, Y., Cao, L., Zhang, C., Bohlscheid, H.: Customer activity sequence classification for debt prevention in social security. Accepted by Journal of Computer Science and Technology (2009)
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Wu, S., Zhao, Y., Zhang, H., Zhang, C., Cao, L., Bohlscheid, H. (2009). Debt Detection in Social Security by Adaptive Sequence Classification. In: Karagiannis, D., Jin, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2009. Lecture Notes in Computer Science(), vol 5914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10488-6_21
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DOI: https://doi.org/10.1007/978-3-642-10488-6_21
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