Tuning data mining methods for cost-sensitive regression: A study in loan charge-off forecasting

Journal of Management Information Systems

Volumn 25, Issue 3, 2009, Pages 315-336

  Bansal, Gaurav ^a   Sinha, Atish P ^b   Zhao, Huimin ^b  

^a UNIVERSITY OF WISCONSIN GREEN BAY   (United States)

^b UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

Author keywords

Asymmetric costs; Cost sensitive regression; Data mining; Forecasting; Loan charge off; Model tuning

Indexed keywords

ASYMMETRIC COSTS; BANK LOANS; CONVEX COST FUNCTION; COST-SENSITIVE; COST-SENSITIVE LEARNING; COST-SENSITIVE REGRESSION; DATA MINING METHODS; EFFICIENT ALGORITHM; LOAN CHARGE-OFF; MODEL TREES; MODEL TUNING; PREDICTION ERRORS; PREDICTIVE DATA MINING; REAL-WORLD; REGRESSION METHOD; REGRESSION MODEL; UNIT COST;

ALGORITHMS; COST FUNCTIONS; COST REDUCTION; EDUCATION; ERRORS; FORECASTING; INFORMATION MANAGEMENT; MINING; TUNING;

REGRESSION ANALYSIS;

EID: 66549085044     PISSN: 07421222     EISSN: None     Source Type: Journal    
DOI: 10.2753/MIS0742-1222250309     Document Type: Article

References (28)

1. anandarajan, M. Profiling Web usage in the workplace: a behavior-based artificial intelligence approach. Journal of Management Information Systems, 19, 1 (Summer 2002), 243-266.
2. Cares, D.C. What's an adequate loan-loss reserve? ABA Banking Journal, 77, 3 (1985), 41-43.
3. Cavallo, M., and Majnoni, G. Do banks provision for bad loans in good times? Empirical evidence and policy implications. Policy research Working Paper, World Bank, Washington, DC, 2001 (available at http://econ.worldbank.org/ external/default/main? pagePK=64165259&theSitePK=469372&piPK= 64165421&menuPK=64166322&entity ID=000094946-01071204123124).
4. Crone, S.F.; lessmann, S.; and Stahlbock, R. utility based data mining for time series analysis: Cost-sensitive learning for neural network predictors. In G. Weiss, M. Saar-tsechansky, and B. Zadrozny (eds.), Proceedings of the First International Workshop on Utility-Based Data Mining. New York: aCM Press, 2005, pp. 59-68.
5. Davids, L.E. Dictionary of Banking and Finance. Lanham, MD: rowman and Littlefield, 1978.
6. Domingos, P. Meta cost: a general method for making classifiers cost sensitive. In U. Fayyad, S. Chaudhuri, and D. Madigan (eds.), Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 1999, pp. 155-164.
7. Downes, J., and Goodman, J.E. Dictionary of Finance and Investment. hauppauge, NY: Barron's Educational Series, 2006.
8. Elkan, C. the foundations of cost-sensitive learning. In B. Nebel (ed.), Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence. San Francisco: Morgan Kaufmann, 2001, pp. 973-978.
9. handorf, W.C., and Zhu, l. Credit risk management and bank size. Commercial Lending Review, 20, 1 (2005), 27-34.
10. henderson, C.C. the economic performance of african-american-owned banks: the role of loan loss provisions. American Economic Review, 89, 2 (1999), 372-376.
11. Jain, B.a., and Nag, B.N. Performance evaluation of neural network decision models. Journal of Management Information Systems, 14, 2 (Fall 1997), 201-216.
12. Keeton, W.r., and Morris, C.S. Why do banks' loan losses differ? Economic Review, 72, 5 (1987), 3-21.
13. Kim, C.N., and Mcleod, r. Expert, linear models, and nonlinear models of expert decision making in bankruptcy prediction: a lens model analysis. Journal of Management Information Systems, 16, 1 (Summer 1999), 189-206.
14. Provost, F.; Fawcett, T.; and Kohavi, R. the case against accuracy estimation for comparing induction algorithms. In J.W. Shavlik (ed.), Proceedings of the Fifteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann, 1998, pp. 445-453.
15. Quinlan, J.r. learning with continuous classes. In N. adams and L. Sterling (eds.), Proceedings of the Fifth Australian Join Conference on Artificial Intelligence. Singapore: World Scientific, 1992, pp. 343-348.
16. rumelhart, D.E.; hinton, G.E.; and Williams, R.J. Learning internal representations by error propagation. In D.E. rumelhart and J.L. McClelland (eds.), Parallel Distributed Processing. Cambridge, Ma: MIt Press, 1986, pp. 318-362.
17. Sarkar, S., and Sriram, r.S. Bayesian models for early warning of bank failures. Management Science, 47, 11 (2001), 1457-1475.
18. Sinha, A.P., and May, J.H. Evaluating and tuning predictive data mining models using receiver operating characteristic curves. Journal of Management Information Systems, 21, 3 (Winter 2004-5), 249-280.
19. Sinkey, J.F. Identifying "problem" banks: how do the banking authorities measure a bank's risk exposure? Journal of Money, Credit and Banking, 10, 2 (1978), 184-193.
20. Sung, T.K.; Chang, N.; and lee, G. Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems, 16, 1 (Summer 1999), 63-86.
21. Tam, K.Y., and Kiang, M.Y. Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38, 7 (1992), 926-947.
22. Thompson, R.D., and Basu, A.P. asymmetric loss functions for estimating system reliability. In D.a. Berry, K.M. Chaloner, and J.K. Geweke (eds.), Bayesian Analysis in Statistics and Econometrics. New York: John Wiley & Sons, 1996, pp. 471-482.
23. Ting, K.M. an instance-weighting method to induce cost-sensitive trees. IEEE Transactions on Knowledge and Data Engineering, 14, 3 (2002), 659-665.
24. Varian, H.R. a Bayesian approach to real estate assessment. In S.E. Fienberg and a. Zellner (eds.), Studies in Bayesian Econometrics and Statistics: In Honor of Leonard J. Savage. Amsterdam: North-holland, 1974, pp. 195-208.
25. Witten, I.h., and Frank, E. Data Mining: Practical Machine Learning Tools and Techniques, 2d ed. San Francisco: Morgan Kaufmann, 2005.
26. Zellner, a. Bayesian estimation and prediction using asymmetric loss functions. Journal of American Statistics Association, 81, 394 (1986) 446-451.
27. Zhao, h. a multi-objective genetic programming approach to developing pareto optimal decision trees. Decision Support Systems, 43, 3 (april 2007), 809-826.
28. Zhou, L.; Burgoon, J.K.; twitchell, D.P.; Qin, T.; and Nunamaker, J. A comparison of classification methods for predicting deception in computer-mediated communication. Journal of Management Information Systems, 20, 4 (Spring 2004), 139-165.