Comprehensible credit scoring models using rule extraction from support vector machines

European Journal of Operational Research

Volumn 183, Issue 3, 2007, Pages 1466-1476

  Martens, David ^a   Baesens, Bart ^a,b   Van Gestel, Tony ^a,c   Vanthienen, Jan ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^c Dexia Group   (Belgium)

Author keywords

Classification; Credit scoring; Rule extraction; Support vector machine

Indexed keywords

CREDIT SCORING MODELS; DATASETS; RULE EXTRACTION;

CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; FEATURE EXTRACTION; FUNCTIONS; MATHEMATICAL MODELS; OPACITY; REAL TIME SYSTEMS;

SUPPORT VECTOR MACHINES;

EID: 34447292534     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2006.04.051     Document Type: Article

References (28)

1. Andrews R., Diederich J., and Tickle A.B. Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8 6 (1995) 373-389
2. Baesens B., Van Gestel T., Viaene S., Stepanova M., Suykens J., and Vanthienen J. Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54 6 (2003) 627-635
3. Baesens B., Setiono R., Mues C., and Vanthienen J. Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science 49 3 (2003) 312-329
4. N. Barakat, J. Diederich, Learning-based rule-extraction from support vector machines. In: 14th International Conference on Computer Theory and Applications ICCTA 2004 Proceedings, Alexandria, Egypt, 2004.
5. Browne A., Hudson B., Whitley D., and Picton P. Biological data mining with neural networks: Implementation and application of a flexible decision tree extraction algorithm to genomic problem domains. Neurocomputing 57 (2004) 275-293
6. M.W. Craven. Extracting comprehensible models from trained neural networks. Ph.D. thesis, University of Winsconsin-Madison, 1996. Supervisor-J.W. Shavlik.
7. Craven M.W., and Shavlik J.W. Extracting tree-structured representations of trained neural networks. Advances in Neural Information Processing Systems 8 (1996) 24-30
8. Cristianini N., and Shawe-Taylor J. An introduction to Support Vector Machines and Other Kernel-Based Learning Methods (2000), Cambridge University Press, New York, NY, USA
9. Drucker H., Wu D., and Vapnik V. Support vector machines for spam categorization. IEEE-NN 10 5 (1999) 1048-1054
10. D.W. Dwyer, A.E. Kocagil, R.M. Stein, Moody's kmv riskcalc v3.1 model, 2004.
11. Fung G., Sandilya S., and Bharat Rao R. Rule extraction from linear support vector machines. KDD '05: Proceeding of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (2005), ACM Press, New York, NY, USA 32-40
12. Van Gestel T., Baesens B., Suykens J., Van den Poel D., Baestaens D.-E., and Willekens M. Bayesian kernel based classification for financial distress detection. European Journal of Operational Research 172 3 (2006) 979-1003
13. T. Van Gestel, J.A.K. Suykens, B. Baesens, S. Viaene, J. Vanthienenand G. Dedene, B. De Moor, J. Vandewalle. Benchmarking least squares support vector machine classifiers. CTEO, Technical Report 0037, K.U. Leuven, Belgium, 2000.
14. Van Gestel T., Suykens J.A.K., Baestaens D.-E., Lambrechts A., Lanckriet G., Vandaele B., De Moor B., and Vandewalle J. Financial time series prediction using least squares support vector machines with the evidence framework. IEEE Transactions on Neural Networks 12 4 (2001) 809-821
15. S. Hettich, S.D. Bay. The uci kdd archive, 1996. .
16. U. Johansson, R. König, L. Niklasson. The truth is in there - rule extraction from opaque models using genetic programming. In: 17th International Florida AI Research Symposium Conference FLAIRS Proceedings, 2004.
17. J.T.Yao. Sensitivity analysis for data mining. In: 22nd International Conference of NAFIPS Proceedings, 2003, pp. 272-277.
18. Koza J.R. Genetic Programming: On the Programming of Computers by Natural Selection (1992), MIT Press, Cambridge, Mass
19. Lu C., Van Gestel T., Suykens J.A.K., Van Huffel S., Vergote I., and Timmerman D. Preoperative prediction of malignancy of ovarium tumor using least squares support vector machines. Artificial Intelligence in Medicine 28 3 (1999) 281-306
20. Mannino M.V., and Koushik M.V. The cost-minimizing inverse classification problem: A genetic algorithm approach. Decision Support Systems 29 3 (2000) 283-300
21. H. Nùnez, C. Angulo, A. Catala, Rule extraction from support vector machines. In: European Symposium on Artificial Neural Networks Proceedings, 2002, pp. 107-112.
22. H. Nùnez, C. Angulo, A. Catala. Rule based learning systems from SVM and RBFNN. Tendencias de la mineria de datos en espana, Red Espaola de Minera de Datos, 2004.
23. Pochet N., De Smet F., Suykens J.A.K., and De Moor B.L.R. Systematic benchmarking of microarray data classification: Assessing the role of non-linearity and dimensionality reduction. Bioinformatics 20 17 (2004) 3185-3195
24. Quinlan J.R. Induction of decision trees. Machine Learning 1 1 (1986) 81-106
25. Quinlan J.R. C4.5 Programs for Machine Learning (1993), Morgan Kaufman Publishers Inc., San Francisco, CA, USA
26. Ripley B.D. Neural networks and related methods for classification. Journal of the Royal Statistical Society B 56 (1994) 409-456
27. Silverman D.W. Density Estimation for Statistics and Data Analysis (1986), Chapman and Hall
28. Vapnik V.N. The Nature of Statistical Learning Theory (1995), Springer-Verlag, New York, Inc., New York, NY, USA