Rule Extraction from Neural Networks and Support Vector Machines for Credit Scoring

Intelligent Systems Reference Library

Volumn 25, Issue , 2012, Pages 299-320

  Setiono, Rudy ^a   Baesens, Bart ^b   Martens, David ^b,c  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c GHENT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84885660668     PISSN: 18684394     EISSN: 18684408     Source Type: Book Series    
DOI: 10.1007/978-3-642-23151-3_13     Document Type: Article

References (36)

1. Andrews, R., Diederich, J., Tickle, A.B.: A survey and critique of techniques for extracting rules from trained neural networks. Knowledge Based Systems 8(6), 373-389 (1995)
2. Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54(6), 627-635 (2003)
3. Baesens, B., Setiono, R., Mues, C., Vanthienen, J.: Using neural network rule extraction and decision tables for credit risk evaluation. Management Science 49(3), 312-329 (2003)
4. Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54(6), 627-635 (2003)
5. Barakat, N., Diederich, J.: Eclectic rule extraction from support vector machines. International Journal of Computational Intelligence 2(1), 59-62 (2005)
6. Barakat, N.H., Bradley, A.P.: Rule extraction from support vector machines: Measuring the explanation capability using the area under the ROC curve. In: Proc. of ICPR, vol. (2), pp. 812-815. IEEE Computer Society, Los Alamitos (2006)
7. Barakat, N.H., Bradley, A.P.: Rule extraction from support vector machines: A sequential covering approach. IEEE Transactions on Knowledge and Data Engineering 19(6), 729-741 (2007)
8. Battiti, R.: First- and second-order methods for learning: Between steepest descent and Newton's method. Neural Computation 4, 141-166 (1992)
9. Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)
10. Cohen, W.W.: Fast effective rule induction. In: Proc. of the 12th International Conference on Machine Learning, pp. 115-123 (1995)
11. Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Machine Learning 15(2), 201-221 (1994)
12. Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New York (2000)
13. Dennis Jr., J.E., Schnabel, R.E.: Numerical methods for unconstrained optimization and nonlinear equations. Prentice Halls, Englewood Cliffs (1983)
14. Downs, T., Gates, K.E., Masters, A.: Exact Simplification of support vector solutions. Journal of Machine Learning Research 2, 293-297 (2001)
15. Fawcett, T.: PRIE: A system for generating rulelists to maximize ROC performance. Data Mining and Knowledge Discovery 17(2), 207-224 (2008)
16. Fung, G., Sandilya, S., Rao, R.B.: Rule Extraction from linear support vector machines. In: Proc. 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 32-40 (2005)
17. Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the theory of neural computation. Addison-Wesley, Redwood City (1991)
18. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359-366 (1989)
19. Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13, 415-425 (2002)
20. Huysmans, J., Baesens, B., Vanthienen, J.: ITER: An algorithm for predictive regression rule extraction. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 270-279. Springer, Heidelberg (2006)
21. Lessmann, S., Baesens, B., Mues, C., Pietsch, S.: Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE Transactions Software Engineering 34(4), 485-496 (2008)
22. Martens, D., Van Gestel, T., Baesens, B.: Decompositional rule extraction from support vector machines by active learning. IEEE Transactions on Knowledge and Data Engineering 21(2), 178-191 (2009)
23. Nũnez, H., Angulo, C., Català, A.: Rule extraction from support vector machines. In: Proc. European Symposium on Artificial Neural Networks (ESANN), pp. 107-112 (2002)
24. Prechelt, L.: PROBEN1 - A set of benchmarks and benchmarking rules for neural network training algorithms. Technical Report 21/94, Fakulẗat f̈ur Informatik. Universiẗat Karlsruhe, Germany. Anonymous ftp, ftp://pub/papers/techreports/1994/1994021.ps.gzonftp.ira.uka.de
25. Quinlan, R.: C4.5: Programs for machine learning. Morgan Kaufman, San Mateo (1993)
26. Saar-Tsechansky, M., Provost, F.: Decision-centric active learning of binaryoutcome models. Information Systems Research 18(1), 4-22 (2007)
27. Setiono, R.: A neural network construction algorithm which maximizes the likelihood function. Connection Science 7(2), 147-166 (1995)
28. Setiono, R., Hui, L.C.K.: Use of quasi-Newton method in a feedforward neural network construction algorithm. IEEE Transactions on Neural Networks 6(2), 326-332 (1995)
29. Setiono, R.: A penalty function approach for pruning feedforward neural networks. Neural Computation 9(1), 185-204 (1997)
30. Setiono, R., Baesens, B., Mues, C.: Recursive neural network rule extraction for data with mixed attributes. IEEE Transactions on Neural Networks 19(2), 299-307 (2008)
31. Sexton, R.S., McMurtrey, S., Cleavenger, D.J.: Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem. European Journal of Operational Research 168, 1009-1018 (2006)
32. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least squares support vector machines. World Scientific, Singapore (2003)
33. Thomas, L., Edelman, D., Crook, J.: Credit scoring and its applications. SIAM, Philadelphia (2002)
34. Tickle, A.B., Andrews, R., Golea, M., Diederich, J.: The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transactions on Neural Networks 9(6), 1057-1068 (1998)
35. Tipping, M.: Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211-244 (2001)
36. Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)