A new approach for measuring rule set consistency

Data and Knowledge Engineering

Volumn 63, Issue 1, 2007, Pages 167-182

  Huysmans, Johan ^a   Baesens, Bart ^a,b   Vanthienen, Jan ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Consistency; Data mining; Machine learning; Model selection; Rule extraction

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; DATA STRUCTURES; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS;

CLASS LABELS; CLASSIFICATION RULES; RULE EXTRACTION;

ALGORITHMS;

EID: 34250198633     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2007.01.001     Document Type: Article

References (20)

1. Equal Credit Opportunity Act, United States Code, title 15, chapter 41, subchapter IV, 1974.
2. Andrews R., Diederich J., and Tickle A. Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge Based Systems 8 6 (1995) 373-389
3. B. Baesens, R. Setiono, C. Mues, S. Viaene, J. Vanthienen, Building credit-risk evaluation expert systems using neural network rule extraction and decision tables, in: Proceedings of the Twenty Second International Conference on Information Systems (ICIS 2001), New Orleans, USA., 2001, pp. 159-168.
4. 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
5. Breiman L., Friedman J., Olsen R., and Stone C. Classification and Regression Trees (1984), Wadsworth and Brooks
6. CIA, Cia factbook retrieved from: .
7. Clark P., and Niblett T. The CN2 induction algorithm. Machine Learning 3 (1989) 261-283
8. Cohen W. Fast effective rule induction. In: Prieditis A., and Russell S. (Eds). Proceedings of the 12th International Conference on Machine Learning (1995), Morgan Kaufman, Tahoe City, CA 115-123
9. Frank E., and Witten I. Generating accurate rule sets without global optimization. Proceedings of the Fifteenth International Conference on Machine Learning (1998), Morgan Kaufman, San Francisco 144-151
10. Huysmans J., Baesens B., and Vanthienen J. ITER: an algorithm for predictive regression rule extraction. Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2006). Lecture Notes in Computer Science vol. 4081 (2006), Springer-Verlag 270-279
11. U. Johansson, R. König, L. Niklasson, Automatically balancing accuracy and comprehensibility in predictive modeling, in: Proceedings of the 8th International Conference on Information Fusion, 2005.
12. Lele S., Golden B., Ozga K., and Wasil E. Clustering rules using empirical similarity of support sets. Proceedings of the 4th International Conference on Discovery Science (2001), Springer-Verlag, London, UK 447-451
13. J. Neumann, Classification and evaluation of algorithms for rule extraction from artificial neural networks, Ph.D. summer project, University of Edingburgh, 1998.
14. D. Newman, S. Hettich, C. Blake, C. Merz, UCI repository of machine learning databases, 1998.
15. Quinlan J. C4.5: Programs for Machine Learning (1993), Morgan Kaufman, San Francisco, CA, USA
16. Thomas L. A survey of credit and behavioural scoring; forecasting financial risk of lending to consumers. International Journal of Forecasting 16 2 (2000) 149-172
17. Towell G., and Shavlik J. The extraction of refined rules from knowledge-based neural networks. Machine Learning 13 1 (1993) 71-101
18. Transparency International, Corruption perceptions index retrieved from: .
19. Vanthienen J., Mues C., and Aerts A. An illustration of verification and validation in the modelling phase of KBS development. Data and Knowledge Engineering 27 3 (1998) 337-352
20. Witten I., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques (2005), Morgan Kaufman, San Francisco