Recursive neural network rule extraction for data with mixed attributes

IEEE Transactions on Neural Networks

Volumn 19, Issue 2, 2008, Pages 299-307

  Setiono, Rudy ^a   Baesens, Bart ^b,c   Mues, Christophe ^c  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Continuous attributes; Credit scoring; Discrete attributes; Rule extraction

Indexed keywords

ALGORITHMS; KNOWLEDGE BASED SYSTEMS;

CONTINUOUS ATTRIBUTES; CREDIT SCORING; DISCRETE ATTRIBUTES; RECURSIVE ALGORITHM; RECURSIVE NEURAL NETWORK; RULE EXTRACTION;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; FEEDBACK SYSTEM; STATISTICAL ANALYSIS;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; FEEDBACK; NEURAL NETWORKS (COMPUTER);

EID: 40549122717     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2007.908641     Document Type: Article

References (31)

1. R. Andrews, J. Diederich, and A. B. Tickle, "A survey and critique of techniques for extracting rules from trained neural networks," Knowl.- Based Syst., vol. 8, no. 6, pp. 373-389, 1995.
2. B. Baesens, R. Setiono, C. Mues, and J. Vanthienen, "Using neural network rule extraction and decision tables for credit-risk evaluation," Manage. Sci., vol. 49, no. 3, pp. 312-329, 2003.
3. J. M. Benitez, J. L. Castro, and I. Requena, "Are neural network black boxes?," IEEE Trans. Neural Netw., vol. 8, no. 5, pp. 1156-1164, Sep. 1997.
4. J. L. Castro, C. J. Mantas, and J. M. Benitez, "Interpretation of artificial neural networks by means of fuzzy rules," IEEE Trans. Neural Netw., vol. 13, no. 1, pp. 101-116, Jan. 1997.
5. M. Craven and J. Shavlik, "Extracting tree-structured representations of trained networks," in Advances in Neural Information Processing Systems(NIPS). Cambridge, MA: MIT Press, 1996, vol. 8, pp. 24-30.
6. T. A. Etchells and J. P. G. Lisboa, "Orthogonal search-based rule extraction (OSRE) for trained neural-networks: A practical and efficient approach," IEEE Trans. Neural Netw., vol. 17, no. 2, pp. 374-384, Mar. 2006.
7. L. Fu, "A neural-network model for learning domain rules based on its activation function characteristics," IEEE Trans. Neural Netw., vol. 9, no. 5, pp. 787-795, Sep. 1998.
8. A. Gupta, S. Park, and S. M. Lam, "Generalized analytic rule extraction for feedforward neural networks," IEEE Trans. Knowl. Data Eng., vol. 11, no. 6, pp. 985-991, Nov./Dec. 1999.
9. W. E. Henley and D. J. Hand, "Construction of a k-nearest neighbor credit-scoring system," IMA J. Math. Appl. Bus. Ind., vol. 8, pp. 305-321, 1997.
10. K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Netw., vol. 2, pp. 359-366, 1989.
11. M. Ishikawa, "Rule extraction by successive regularization," Neural Netw., vol. 13, pp. 1171-1183, 2000.
12. E. Kolman and M. Margaliot, "Knowledge extraction from neural networks using the all-permutations fuzzy rule base: The LED display recognition problem," IEEE Trans. Neural Netw., vol. 18, no. 3, pp. 925-931, May 2007.
13. E. Kolman and M. Margaliot, "Are artificial neural networks white boxes?," IEEE Trans. Neural Netw., vol. 16, no. 4, pp. 844-852, Jul. 2005.
14. H. Liu and S. T. Tan, "X2R: A fast rule generator," in Proc. IEEE Int. Conf. Syst., Man, Cybern., New York, 1995, pp. 1631-1635.
15. S. Mitra and Y. Hayashi, "Neuro-fuzzy rule generation: Survey in soft computing framework," IEEE Trans. Neural Netw., vol. 11, no. 3, pp. 748-768, May 2000.
16. D. Nauck and R. Kruse, "Neuro-fuzzy methods in fuzzy rule generation," in Fuzzy Sets in Approximate Reasoning and Information Systems. Norwell, MA: Kluwer, 1999, pp. 305-334.
17. L. Prechelt, "PROBEN1 - A set of benchmarks and benchmarking rules for neural network training algorithms," Fakultät für Informatik, Universität Karlsruhe, Karlsruhe, Germany, Tech. Rep. 21/94.
18. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufman, 1993.
19. J. R. Rabuñal, J. Dorado, A. Pazos, J. Periera, and D. Rivero, "A new approach to the extraction of ANN rules and to their generalization capacity through GP," Neural Comput., vol. 16, no. 7, pp. 1483-1523, 2004.
20. R. Setiono, "Extracting rules from neural networks by pruning and hidden-unit splitting," Neural Comput., vol. 9, no. 1, pp. 205-225, 1997.
21. R. Setiono, "A penalty-function approach for pruning feedforward neural networks," Neural Comput., vol. 9, no. 1, pp. 185-204, 1997.
22. R. Setiono and H. Liu, "Symbolic representation of neural networks," IEEE Computer, vol. 29, no. 3, pp. 71-77, Mar. 1996.
23. R. Setiono, "Extracting M-of-N rules from trained neural networks," IEEE Trans. Neural Netw., vol. 11, no. 2, pp. 512-519, Mar. 2000.
24. R. Setiono and H. Liu, "Neurolinear: From neural networks to oblique decision rules," Neurocomputing, vol. 17, no. 1, pp. 1-24, 1997.
25. R. Setiono and H. Liu, "A connectionist approach to generating oblique decision trees," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 29, no. 3, pp. 440-444, Jun. 1999.
26. R. S. Sexton, S. McMurtrey, and D. J. Cleavenger, "Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem," Eur. J. Operat. Res., vol. 168, pp. 1009-1018, 2006.
27. M. Smith, Neural Networks for Statistical Modeling. New York: Van Nostrand Reinhold, 1993.
28. I. A. Taha and J. Ghosh, "Symbolic interpretation of artificial neural networks," IEEE Trans. Knowl. Data Eng., vol. 11, no. 3, pp. 448-463, May/Jun. 1999.
29. S. B. Thrun et al., "The MONK's problems - A performance comparison of different learning algorithm," Carnegie Mellon Univ., Pittsburgh, PA, 1991, CMU-CS-91-197, to be published.
30. G. G. Towell and J. W. Shavlik, "Extraction of refined rules from knowledge-based neural networks," Mach. Learn., vol. 13, no. 1, pp. 71-101, 1993.
31. H. Tsukimoto, "Extracting rules from trained neural networks," IEEE Trans. Neural Netw., vol. 11, no. 2, pp. 377-389, Mar. 2000.