Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal

Expert Systems with Applications

Volumn 42, Issue 13, 2015, Pages 5737-5753

  Florez Lopez, Raquel ^a   Ramon Jeronimo, Juan Manuel ^a  

^a UNIVERSIDAD PABLO DE OLAVIDE   (Spain)

Author keywords

Credit scoring; Decision forests; Diversity; Ensemble strategies; Gradient boosting; Random forests

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; LEARNING ALGORITHMS; LEARNING SYSTEMS;

CREDIT SCORING; DECISION FOREST; DIVERSITY; ENSEMBLE STRATEGIES; GRADIENT BOOSTING; RANDOM FORESTS;

RISK ASSESSMENT;

EID: 84926638352     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2015.02.042     Document Type: Article

References (78)

1. J. Abellan, and C. Mantas Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring Expert Systems with Applications 41 2014 3825 3830
2. J. Abellan, and A.R. Masegosa Bagging schemes on the presence of class noise in classification Expert Systems with Applications 39 2012 6827 6837
3. E.I. Altman Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy The Journal of Finance 23 1968 589 609
4. 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
5. Basel Committee on Banking Supervision (BCBS). (2011, June). Basel III: A global regulatory framework for more resilient banks and banking systems. Basel: Bank for International Settlements.
6. L. Breiman, J.H. Freidman, R.A. Olshen, and C.J. Stone Classification and regression trees 1984 Wadsworth Belmont
7. L. Breiman Bagging predictors Machine Learning 24 1996 123 140
8. L. Breiman Random forests Machine Learning 45 2001 5 32
9. I. Brown, and C. Mues An experimental comparison of classification algorithms for imbalanced credit scoring data sets Expert Systems with Applications 39 2012 3446 3453
10. B. Cestnik, I. Kononenko, and I. Bratko ASSISTANT 86: A knowledge elicitation tool for sophisticated users I. Bratko, N. Lavrac, Progress in machine learning 1987 Sigma Press Wilmslow
11. Y.-S. Chen, and C.-H. Cheng Hybrid models based on rough set classifiers for setting credit risk rating decision rules in the global banking industry Knowledge-Based Systems 39 2013 224 239
12. J.N. Crook, D.B. Edelman, and L.C. Thomas Recent developments in consumer credit risk assessment European Journal of Operational Research 183 2007 1447 1465
13. P. Danenas, and G. Garsva Selection of support vector machines based classifiers for credit risk domain Expert Systems with Applications 42 2015 3194 3204
14. M. Daubie, P. Levecq, and N. Meskens A comparison of the rough sets and recursive partitioning induction approaches: An application to commercial loans International Transactions in Operational Research 9 2002 681 694
15. K.W. De Bock, and D. Van den Poel Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models Expert Systems with Applications 39 2012 6816 6826
16. E.R. DeLong, D.M. DeLong, and D.L. Clarke-Pearson Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach Biometrics 44 1988 837 845
17. J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30
18. G. Derelioglu, and F. Gürgen Knowledge discovery using neural approach for SME's credit risk analysis problem in Turkey Expert Systems with Applications 38 2011 9313 9318
19. T.G. Dietterich Ensemble methods in machine learning J. Kittler, F. Roli, First international workshop on multiple classifier systems 2000 Springer Verlag New York 1 15
20. Efron, B., & Tibshirani, R.J. (1995). Cross-validation and the bootstrap: Estimating the error rate of a prediction rule. Technical Report 176, Stanford University.
21. B. Efron, and R.J. Tibshirani An introduction to the bootstrap 1993 Chapman and Hall New York
22. E. Fedorova, E. Gilenko, and S. Dovzhenko Bankruptcy prediction for Russian companies: Application of combined classifiers Expert Systems with Applications 40 2013 7285 7293
23. D. Feldman, and D. Gross Mortgage default: Classification trees analysis The Journal of Real State Finance and Economics 30 4 2005 369 396
24. S. Finlay Multiple classifier architectures and their applications to credit risk assessment European Journal of Operational Research 210 2011 368 378
25. M. Friedman A comparison of alternative tests of significance for the problem of m rankings Annals of Mathematical Statistics 11 1940 86 92
26. Friedman, J.H. (1999a, February). Greedy function approximation: A gradient boosting machine. Technical Document, Stanford University.
27. Friedman, J.H. (1999b, March). Stochastic gradient boosting. Technical Document, Stanford University.
28. M.J. Gacto, R. Alcala, and F. Herrera Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures Information Sciences 181 2011 4340 4360
29. R. Geng, I. Bose, and X. Chen Prediction of financial distress: An empirical study of listed Chinese companies using data mining European Journal of Operational Research 241 2015 236 247
30. D.J. Hand Classifier technology and the illusion of progress Statistical Science 21 2006 1 15
31. D.J. Hand Measuring classifier performance. A coherent alternative to the area under the ROC curve Machine Learning 77 2009 103 123
32. B. Hanczar, J. Hua, C. Sima, J. Weinstein, M. Bittner, and E.R. Dougherty Small-sample precision of ROC-related estimates Bioinformatics 26 2010 822 830
33. W. Härdle, R. Moro, and D. Schafer Predicting bankruptcy with support vector machines P. Cizek, W. Härdle, R. Weron, Statistical tools for finance and insurance 2005 Springer Berlin 225 248
34. T. Harris Credit scoring using the clustered support vector machine Expert Systems with Applications 42 2 2015 741 750
35. W.E. Henley, and D.J. Hand Statistical classification methods in consumer credit scoring: A review Journal of the Royal Statistical Society, Series A 160 3 1997 523 541
36. Ho, T.K. (1998). C4.5 decision forest. In Proceedings of the 14th international conference on pattern recognition (pp. 545-549), Brisbane, Australia.
37. N.-C. Hsieh, and L.-P. Hung A data driven ensemble classifier for credit scoring analysis Expert Systems with Applications 37 2010 534 545
38. C.L. Huang, M.C. Chen, and C.J. Wang Credit scoring with a data mining approach based on support vector machines Expert Systems with Applications 33 2007 847 856
39. C. Hung, and J.H. Chen A selective ensemble based on expected probabilities for bankruptcy prediction Expert Systems with Applications 36 2009 5297 5303
40. G.V. Kass An exploratory technique for investigating large quantities of categorical data Applied Statistics 29 2 1980 119 127
41. K. Kestens, P. Van Cauwenberge, and H.V. Vauwedhe Trade credit and company performance during the 2008 financial crisis Accounting and Finance 52 2012 1125 1151
42. J.W. Kim Expert systems for bond rating: A comparative analysis of statistical, rule-based and neural network systems Expert Systems 10 1993 167 171
43. M.-J. Kim, D.-K. Kang, and H.B. Kim Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction Expert Systems with Applications 42 2015 1074 1082
44. L.I. Kuncheva, and C.J. Whitaker Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy Machine Learning 51 2 2003 181 207
45. J. Kruppa, A. Schwarz, G. Arminger, and A. Ziegler Consumer credit risk: Individual probability estimates using machine learning Expert Systems with Applications 40 2013 5125 5131
46. T.-S. Lee, and I.F. Chen A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines Expert Systems with Applications 28 4 2005 743 752
47. S. Li, I.W. Tsang, and N.S. Chaudhari Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis Expert Systems with Applications 39 2012 4947 4953
48. A.I. Marques, V. Garcia, and J.S. Sanchez Exploring the behaviour of base classifiers in credit scoring ensembles Expert Systems with Applications 39 2012 10244 10250
49. A.I. Marques, V. Garcia, and J.S. Sanchez Two-level classifier ensembles for credit risk assessment Expert Systems with Applications 39 2012 10916 10922
50. D. Martens, B. Baesens, T. Van Gestel, and J. Vanthienen Comprehensible credit scoring models using rule extraction from support vector machines European Journal of Operational Research 183 3 2007 1466 1476
51. B.H. Menze, B.M. Kelm, D.N. Splitthoff, U. Koethe, and F.a. Hamprecht On oblique random forests D. Gunopulos, T. Hofmann, D. Malerba, M. Vazirgiannis, Machine learning and knowledge discovery in databases 2011 Springer Berlin 453 469
52. C. Mues, B. Baesens, C.M. Files, and J. Vanthienen Decision diagrams in machine learning: an empirical study on real-life credit-risk data Expert Systems with Applications 27 2004 257 264
53. L. Nanni, and A. Lumini An experimental comparison of ensemble classifiers for bankruptcy prediction and credit scoring Expert Systems with Applications 36 2009 3028 3033
54. Nemenyi, P.B. (1963). Distribution-free multiple comparisons [Ph.D. thesis]. Princeton University.
55. 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
56. G. Paleologo, A. Eliseeff, and G. Antonini Subagging for credit scoring models European Journal of Operational Research 201 2010 490 499
57. J.R. Quinlan Discovering rules by induction from large collections of examples D. Michie, Expert systems in the micro electronic age 1979 Edinburg University Press Edinburgh
58. J.R. Quinlan C4.5: Programs for machine learning 1993 Morgan Kaufmann Los Altos, CA
59. J.J. Rodriguez, L.I. Kuncheva, and C.J. Alonso Rotation forests: A new classifier ensemble method IEEE Transactions on Pattern Analysis and Machine Intelligence 28 10 2006 1619 1630
60. R.E. Schapire The strength of weak learnability Machine Learning 5 1990 197 227
61. R. Setiono, B. Baesens, and C. Mues Rule extraction from minimal neural networks for credit card screening International Journal of Neural Systems 21 4 2011 265 276
62. M. Seyedhosseini, and T. Tasdizen Disjunctive normal random forests Pattern Recognition 48 2015 976 983
63. J. Sun, H. Li, Q.-H. Huang, and K.Y. He Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches Knowledge-Based Systems 57 2014 41 56
64. J. Sun, and H. Li Financial distress prediction using support vector machines: Ensemble vs. individual Applied Soft Computing 12 2012 2254 2265
65. C.-F. Tsai, Y.-F. Hsu, and D.C. Yen A comparative study of classifier ensembles for bankruptcy prediction Applied Soft Computing 24 2014 977 984
66. J.M. Tomczak, and M. Zieba Classification restricted Boltzmann machine for comprehensible credit scoring model Expert Systems with Applications 42 2015 1789 1796
67. B. Twala Multiple classifier application to credit risk assessment Expert Systems with Applications 37 2010 3326 3336
68. G. Wang, and J. Ma Study of corporate credit risk prediction based on integrating boosting and random subspace Expert Systems with Applications 38 2011 13871 13878
69. G. Wang, and J. Ma A hybrid ensemble approach for enterprise credit risk assessment based on support vector machine Expert Systems with Applications 39 2012 5325 5331
70. G. Wang, J. Hao, J. Ma, and H. Jiang A comparative assessment of ensemble learning for credit scoring Expert Systems with Applications 38 2011 223 230
71. G. Wang, J. Ma, L. Huang, and K. Xu Two credit scoring models based on dual strategy ensemble trees Knowledge-Based Systems 26 2012 61 68
72. G. Wang, J. Ma, and S. Yang An improved boosting based on feature selection for corporate bankruptcy prediction Expert Systems with Applications 41 2014 2353 2361
73. D. West Neural networks credit scoring models Computers & Operations Research 22 2000 1131 1152
74. D. Wolpert Stacked generalization Neural Networks 5 1992 241 259
75. T.-H. Wu, and M.-F. Hu Credit risk assessment and decision making by a fusion approach Knowledge-Based Systems 35 2012 102 110
76. L. Yu, W. Yue, S. Wang, and K.K. Lai Support vector machine based multiagent ensemble learning for credit risk evaluation Expert Systems with Applications 37 2010 1351 1360
77. L. Yu, S. Wang, and K.K. Lai Credit risk assessment with a multistage neural network ensemble learning approach Expert Systems with Applications 34 2008 1434 1444
78. L. Zhou, K.K. Lai, and L. Yu Least squares support vector machines ensemble models for credit scoring Expert Systems with Applications 37 2010 127 133