Greedy rule generation from discrete data and its use in neural network rule extraction

Neural Networks

Volumn 21, Issue 7, 2008, Pages 1020-1028

  Odajima, Koichi ^a   Hayashi, Yoichi ^a   Tianxia, Gong ^b   Setiono, Rudy ^b  

^a MEIJI UNIVERSITY   (Japan)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Classification; Clustering; Discretization; Neural networks; Rule generation

Indexed keywords

ARTIFICIAL INTELLIGENCE; BOOLEAN FUNCTIONS; CLASSIFICATION (OF INFORMATION); COMPUTER NETWORKS; IMAGE CLASSIFICATION; SET THEORY; VEGETATION;

CLASSIFICATION; CLUSTERING; DISCRETIZATION; RULE GENERATION;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION ALGORITHM; DATA EXTRACTION; GREEDY RULE GENERATION; MEDICAL INFORMATICS; PRIORITY JOURNAL; SAMPLE (STATISTICS); TECHNIQUE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; FLOWERS; HUMANS; NEOPLASMS; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; SOFTWARE;

EID: 51049095878     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2008.01.003     Document Type: Article

References (32)

1. 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
2. Blake, C.L., & Merz, C.J. (1998). UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. http://www.ics.uci.edu/~mlearn/MLRepository.html
3. Bologna G. A model for single and multiple knowledge based neural networks. Artificial Intelligence in Medicine 28 2 (2003) 141-163
4. Bradshaw C.J.A., Davis L.S., Purvis M., Zhou Q., and Benwell G.L. Using artificial neural networks to model the suitability of coastline for breeding by New Zealand fur seals (Arctocephalus forsteri). Ecological Modeling 148 2 (2002) 111-131
5. Browne A., Hudson B.D., Whitely D.C., Ford M.G., 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. Castro J.L., Mantas C.J., and Benitez J. Interpretation of artificial neural networks by means of fuzzy rules. IEEE Transactions on Neural Networks 13 1 (2002) 101-116
7. Clark P., and Niblett T. The CN2 Induction Algorithm. Machine Learning Journal 3 4 (1989) 261-283
8. Duch W., Adamczak R., and Grabczekski K. A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE Transactions on Neural Networks 12 2 (2001) 277-306
9. Elalfi A.E., Haque R., and Elalami M.E. Extracting rules from trained neural network using GA for managing E-business. Applied Soft Computing 4 1 (2004) 65-77
10. Fisher R.A. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7 (1936) 179-188
11. Fu L.M. Rule generation from neural networks. IEEE Transactions on Systems, Man and Cybernetics 243 8 (1994) 173-182
12. Fukumi, M., & Akamatsu, N. (1999). A new rule extraction method from neural networks. In Proceedings of IJCNN99 (pp. 1-5)
13. Huang S.H., and Xing H. Extract intelligible and fuzzy rules from neural networks. Fuzzy Sets and Systems 132 2 (2002) 233-243
14. Ishikawa M. Rule extraction by successive regularization. Neural Networks 13 10 (2000) 1171-1183
15. Krishnan R., Sivakumar G., and Bhattacharya P. A search technique for rule extraction from trained neural networks. Pattern Recognition Letters 20 3 (1999) 273-280
16. Liu, H., & Setiono, R. (1995). Chi2: Feature selection and discretization of numeric attributes. In Proceedings of the 7th international conference on tools for artificial intelligence (pp. 388-391)
17. Liu, H., & Tan, S.T. (1995). X2R: A fast rule generator. In Proceedings of the 7th IEEE international conference on systems, Man and Cybernetics (pp. 631-635)
18. Merz, C.J., & Murphy, P.M. (1996). UCI Repository of machine learning databases. Irvine, CA: Department of Information and Computer Science, University of California. Available from: http://www.ics.uci.edu/mlearn/mlrepository.html
19. Michalski, R.S., Mozetic, I., Hong, J., & Lavrac, N. (1986). The multi-purpose incremental learning AQ15 and its testing application to three medical domains. In Proceedings of the 5th national conference on AI (pp. 1041-1045)
20. Mitchell T. Machine learning (1997), The Mc Graw-Hill Companies Inc, New York
21. Quinlan R. Programs for machine learning (1993), Morgan Kaufmann Publisher Inc, California
22. Remm J.-F., and Alexandre F. Knowledge extraction using artificial neural networks: Application to radar identification. Signal Processing 82 1 (2002) 117-120
23. Rivest R.L. Learning decision lists. Machine Learning 2 3 (1987) 229-246
24. Setiono R. A penalty function approach for pruning feedforward neural networks. Neural Computation 9 1 (1995) 185-204
25. Setiono R., and Liu H. Symbolic representation of neural networks. IEEE Computer 29 3 (1996) 71-77
26. Setiono R., and Liu H. NeuroLinear: From neural networks to oblique decision rules. Neurocomputing 17 1 (1997) 1-24
27. Setiono R., and Liu H. Neural-network feature selector. IEEE Transactions on Neural Networks 8 3 (1997) 654-662
28. Setiono R. Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence in Medicine 18 3 (2000) 205-219
29. Setiono R., Leow W.K., and Zurada J. Extraction of rules from artificial neural networks for nonlinear regression. IEEE Transactions on Neural Networks 13 3 (2002) 564-577
30. Tickle A.B., Andrews R., Golea M., and 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 (1998) 1057-1068
31. Tsukimoto H. Extracting rules from trained neural networks. IEEE Transactions on Neural Networks 11 2 (2000) 377-389
32. Zhou Z.-H., Jiang Y., and Chen S.-F. Extracting symbolic rules from trained neural network ensembles. AI Communications 16 1 (2003) 3-15