Abstract
The concept of the new hybrid method for debt portfolio repayment prediction has been presented and examined. The method provides functionality for repayment value prediction over time that describes the recovery profile of the debt portfolio. Experimental studies on hybrid combination of various data mining methods like clustering and decision trees into one complex process revealed usefulness of the proposed method for claim appraisals.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aburto, L., Weber, R.: A Sequential Hybrid Forecasting System for Demand Prediction. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 518–532. Springer, Heidelberg (2007)
Ali, S., Smith, K.: On learning algorithm selection for classification. Applied Soft Computing 6(2), 119–138 (2006)
Chou, C.-H., Lin, C.-C., Liu, Y.-H., Chang, F.: A prototype classification method and its use in a hybrid solution for multiclass pattern recognition. Pattern Recognition 39(4), 624–634 (2006)
Eastwood, M., Gabrys, B.: Building Combined Classifiers. In: Nguyen, N.T., Kolaczek, G., Gabrys, B. (eds.) A book chapter in Knowledge Processing and Reasoning for Information Society, pp. 139–163. EXIT Publishing House, Warsaw (2008)
Gabrys, B., Ruta, D.: Genetic algorithms in classifier fusion. Applied Soft Computing 6(4), 337–347 (2006)
Kazienko, P., Musiał, K., Kajdanowicz, T.: Multidimensional Social Network and Its Application to the Social Recommender System. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans (2009) (accepted)
Keles, A., Kolcak, M., Keles, A.l.: The adaptive neuro-fuzzy model for forecasting the domestic debt. Knowledge-Based Systems 21(8), 951–957 (2008)
Kuncheva, L.: Combining Pattern Classifiers. In: Methods and Algorithms. John Wiley & Sons, Inc., Chichester (2004)
Lin, P.-C., Chen, J.-S.: A genetic-based hybrid approach to corporate failure prediction. International Journal of Electronic Finance 2(2), 241–255 (2008)
Ravi, V., Kurniawan, H., Nwee, P., Kumar, R.: Soft computing system for bank performance prediction. Applied Soft Computing 8(1), 305–315 (2008)
Rud, O.: Data Mining Cookbook. In: Modeling Data for Marketing, Risk, and Customer Relationship Management, John Wiley & Sons, Inc., Chichester (2001)
Swanson, N.R., White, H.: A model selection approach to assessing the information in the term structure using linear model and the artificial neural network. Journal of Business and Economics Statistics 13, 265–275 (1995)
Zurada, J., Lonial, S.: Comparison Of The Performance Of Several Data Mining Methods For Bad Debt Recovery In The Healthcare Industry. The Journal of Applied Business Research 21(2), 37–53 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kajdanowicz, T., Kazienko, P. (2009). Hybrid Repayment Prediction for Debt Portfolio. In: Nguyen, N.T., Kowalczyk, R., Chen, SM. (eds) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. ICCCI 2009. Lecture Notes in Computer Science(), vol 5796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04441-0_74
Download citation
DOI: https://doi.org/10.1007/978-3-642-04441-0_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04440-3
Online ISBN: 978-3-642-04441-0
eBook Packages: Computer ScienceComputer Science (R0)