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An ensemble-based model for two-class imbalanced financial problem

Author

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  • Liao, Jui-Jung
  • Shih, Ching-Hui
  • Chen, Tai-Feng
  • Hsu, Ming-Fu

Abstract

This study proposes an ensemble-based model (EBM) for the two-class imbalanced classification problem by joining together the support vector machine (SVM), multiple feature selection combination, back-propagation neural network (BPNN) ensemble, and rough set theory (RST). To improve the significance of the rare and specific region belonging to the minority class in the decision region, we take the SVM as a pre-processor to balance the training dataset and use multiple feature selection combination grounded on ensemble learning in order to determine the most representative features from the re-sized dataset. The representative features are then fed into the BPNN ensemble to construct an effective financial pre-warning mechanism. Lacking comprehensibility and readability is one of the fatal weaknesses of an ensemble classifier and it impedes its real-life application. Thus, the study executes RST to extract knowledge from the BPNN ensemble for decision makers to make suitable judgments. Decision makers can take the decision rules as a roadmap to modify a firm's capital structure so as to survive in an extremely turbulent financial market. Empirical results reveal that the introduced EBM's prediction accuracy is very promising in financial risk mining, relative to other detection approaches in this study.

Suggested Citation

  • Liao, Jui-Jung & Shih, Ching-Hui & Chen, Tai-Feng & Hsu, Ming-Fu, 2014. "An ensemble-based model for two-class imbalanced financial problem," Economic Modelling, Elsevier, vol. 37(C), pages 175-183.
  • Handle: RePEc:eee:ecmode:v:37:y:2014:i:c:p:175-183
    DOI: 10.1016/j.econmod.2013.11.013
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    References listed on IDEAS

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    1. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(03), pages 757-770, September.
    2. Louzis, Dimitrios P. & Vouldis, Angelos T., 2012. "A methodology for constructing a financial systemic stress index: An application to Greece," Economic Modelling, Elsevier, vol. 29(4), pages 1228-1241.
    3. Chi, Li-Chiu, 2009. "Contagion and competitive effects of plan confirmation of reorganization filings: Evidence from the Taiwan Stock Market," Economic Modelling, Elsevier, vol. 26(2), pages 364-369, March.
    4. repec:dau:papers:123456789/8773 is not listed on IDEAS
    5. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    6. Pawlak, Zdzislaw, 2002. "Rough sets, decision algorithms and Bayes' theorem," European Journal of Operational Research, Elsevier, vol. 136(1), pages 181-189, January.
    7. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    8. Gupta, Rangan & Modise, Mampho P., 2012. "South African stock return predictability in the context data mining: The role of financial variables and international stock returns," Economic Modelling, Elsevier, vol. 29(3), pages 908-916.
    9. Chevallier, Julien, 2012. "Global imbalances, cross-market linkages, and the financial crisis: A multivariate Markov-switching analysis," Economic Modelling, Elsevier, vol. 29(3), pages 943-973.
    10. Öğüt, Hulisi & Doğanay, M. Mete & Ceylan, Nildağ Başak & Aktaş, Ramazan, 2012. "Prediction of bank financial strength ratings: The case of Turkey," Economic Modelling, Elsevier, vol. 29(3), pages 632-640.
    11. Vouldis, Angelos T. & Michaelides, Panayotis G. & Tsionas, Efthymios G., 2010. "Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML," Economic Modelling, Elsevier, vol. 27(3), pages 697-704, May.
    12. Chi, Li-Chiu & Tang, Tseng-Chung, 2007. "Impact of reorganization announcements on distressed-stock returns," Economic Modelling, Elsevier, vol. 24(5), pages 749-767, September.
    13. Naifar, Nader, 2012. "Modeling the dependence structure between default risk premium, equity return volatility and the jump risk: Evidence from a financial crisis," Economic Modelling, Elsevier, vol. 29(2), pages 119-131.
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    Cited by:

    1. Hayashi, Yoichi, 2016. "Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective," Operations Research Perspectives, Elsevier, vol. 3(C), pages 32-42.
    2. Wanke, Peter & Barros, Carlos Pestana, 2016. "Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach," Economic Modelling, Elsevier, vol. 53(C), pages 8-22.

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