Two-level classifier ensembles for credit risk assessment

Expert Systems with Applications

Volumn 39, Issue 12, 2012, Pages 10916-10922

  Marques A I ^a   Garcia V ^a   Sanchez J S ^a  

^a UNIVERSITAT JAUME I   (Spain)

Author keywords

Bagging; Boosting; Classifier ensemble; Credit scoring; Random subspace; Rotation forest

Indexed keywords

BAGGING; BOOSTING; CLASSIFIER ENSEMBLES; CREDIT SCORING; RANDOM SUBSPACES;

ADAPTIVE BOOSTING; ARTIFICIAL INTELLIGENCE; FORESTRY; MATHEMATICAL MODELS; STATISTICAL TESTS;

RISK ASSESSMENT;

FORESTRY; MATHEMATICAL MODELS; RANDOM PROCESSES; RISK ASSESSMENT; STATISTICAL ANALYSIS; TESTING;

EID: 84861183555     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.03.033     Document Type: Article

References (33)

1. H.A. Abdou, and J. Pointon Credit scoring, statistical techniques and evaluation criteria: A review of the literature Intelligent Systems in Accounting, Finance & Management 18 2-3 2011 59 88
2. K.M. Ali, and M.J. Pazzani Error reduction through learning multiple descriptions Machine Learning 24 3 1996 173 202
3. B. Baesens, T. van Gestel, S. Viaene, M. Stepanova, J. Suykens, and J. Vanthienen Benchmarking state-of-the-art classification algorithms for credit scoring Journal of the Operational Research Society 54 6 2003 627 635
4. S. Bian, and W. Wang On diversity and accuracy of homogeneous and heterogeneous ensembles International Journal of Hybrid Intelligent Systems 4 2 2007 103 128
5. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140
6. J. Caouette, E. Altman, P. Narayanan, and R. Nimmo Managing credit risk: The great challenge for global financial markets 2008 Wiley Hoboken, NJ
7. B.-W. Chi, and C.-C. Hsu A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model Expert Systems with Applications 39 3 2012 2650 2661
8. J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 1 2006 1 30
9. T.G. Dietterich Machine-learning research: Four current directions AI Magazine 18 4 1997 97 136
10. M. Doumpos, and C. Zopounidis Model combination for credit risk assessment: A stacked generalization approach Annals of Operations Research 151 1 2007 289 306
11. Freund, Y.; Schapire, R. E. (1996). Experiments with a new boosting algorithm. In: Proc. of the 13th international conference on machine learning, Bari, Italy (pp. 148-156).
12. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The WEKA data mining software: An update SIGKDD Explorations Newsletter 11 1 2009 10 18
13. D.J. Hand Good practice in retail credit scorecard assessment Journal of the Operational Research Society 56 9 2005 1109 1117
14. D.J. Hand, and W.E. Henley Statistical classification methods in consumer credit scoring: A review Journal of the Royal Statistical Society: Series A 160 3 1997 523 541
15. T.K. Ho The random subspace method for constructing decision forests IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 1998 832 844
16. C.-L. Huang, M.-C. Chen, and C.-J. Wang Credit scoring with data mining approach based on support vector machines Expert Systems and Applications 33 2007 847 856
17. Z. Huang, H. Chen, C.-J. Hsu, W.-H. Chen, and S. Wu Credit rating analysis with support vector machines and neural networks: A market comparative study Decision Support Systems 37 4 2004 543 558
18. H. Ince, and B. Aktan A comparison of data mining techniques for credit scoring in banking: A managerial perspective Journal of Business Economics and Management 10 3 2009 233 240
19. L.I. Kuncheva Combining classifiers: Soft computing solutions S.K. Pal, A. Pal, Pattern recognition: From classical to modern approaches 2003 World Scientific 427 449
20. L.I. Kuncheva Combining pattern classifiers: Methods and Algorithms 2004 Wiley Hoboken, NJ
21. J.-S. Lee, and D. Zhu When costs are unequal and unknown: A subtree grafting approach for unbalanced data classification Decision Sciences 42 4 2011 803 829
22. D. Martens, T. van Gestel, M. de Backer, R. Haesen, J. Vanthienen, and B. Baesens Credit rating prediction using ant colony optimization Journal of the Operational Research Society 61 4 2010 561 573
23. C.-S. Ong, J.-J. Huang, and G.-H. Tzeng Building credit scoring models using genetic programming Expert Systems with Applications 29 1 2005 41 47
24. Pietruszkiewicz, W. (2008). Dynamical systems and nonlinear Kalman filtering applied in classification. In: Proc. of seventh IEEE international conference on cybernetic intelligent systems, London, UK (pp. 263-268).
25. Provost, F.; Fawcett, T. (1997). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In: Proc. of the third international conference on knowledge discovery and data mining, Newport Beach, CA (pp. 43-48).
26. J.J. Rodriguez, L.I. Kuncheva, and C.J. Alonso Rotation forest: A new classifier ensemble method IEEE Transactions on Pattern Analysis and Machine Intelligence 28 10 2006 1619 1630
27. E. Rosenberg, and A. Gleit Quantitative methods in credit management: A survey Operations Research 42 4 1994 589 613
28. Sabzevari, H.; Soleymani, M.; Noorbakhsh, E. (2007). A comparison between statistical and data mining methods for credit scoring in case of limited available data. In: Proc. of the third CRC credit scoring conference, Edinburgh, UK.
29. M. Sokolova, and G. Lapalme A systematic analysis of performance measures for classification tasks Information Processing and Management 45 4 2009 427 437
30. L.C. Thomas, D.B. Edelman, and J.N. Crook Credit scoring and its applications 2002 SIAM Philadelphia, PA
31. B. Twala Multiple classifier application to credit risk assessment Expert Systems with Applications 37 4 2010 3326 3336
32. G. Wang, J. Hao, J. Ma, and H. Jiang A comparative assessment of ensemble learning for credit scoring Expert Systems with Applications 38 1 2011 223 230
33. D. West, S. Dellana, and J. Qian Neural network ensemble strategies for financial decision applications Computers and Operations Research 32 10 2005 2543 2559