Decision diagrams in machine learning: An empirical study on real-life credit-risk data

Expert Systems with Applications

Volumn 27, Issue 2, 2004, Pages 257-264

  Mues, Christophe ^a   Baesens, Bart ^a,b   Files, Craig M ^c   Vanthienen, Jan ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^c NONE   (United States)

Author keywords

Credit scoring; Data mining; Decision diagrams

Indexed keywords

DATA MINING; DATA REDUCTION; DECISION MAKING; EXPERT SYSTEMS; MARKETING; MATHEMATICAL MODELS; TREES (MATHEMATICS);

CREDIT SCORING; DECISION DIAGRAM; DECISION TREE;

LEARNING SYSTEMS;

EID: 2942590761     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2004.02.001     Document Type: Article

References (22)

1. Baesens B., Setiono R., Mues C., Vanthienen J. Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science. 49:(3):2003;312-329.
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):2003;627-635.
3. Bryant R.E. Graph-based algorithms for boolean function manipulation. IEEE Transactions on Computers. C-35:(8):1986;677-691.
4. Craven M.W., Shavlik J.W. Extracting tree-structured representations of trained networks. Touretzky D., Mozer M., Hasselmo M. Advances in neural information processing systems (NIPS). Vol. 8:1996;24-30 MIT Press, Cambridge, MA.
5. David R.H., Edelman D.B., Gammerman A.J. Machine learning algorithms for credit-card applications. IMA Journal of Mathematics Applied in Business and Industry. 4:1992;43-51.
6. Desai V.S., Crook J.N., Overstreet G.A. Jr. A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research. 95:(1):1996;24-37.
7. Elomaa T., Kääriäinen M. On the practice of branching program boosting. Lecture Notes in Artificial Intelligence. 2167:2001;133-144.
8. Fayyad, U. M., Irani, K. B (1993). Multi-interval discretization of continuous-valued attributes for classification learning. Proceedings of the 13th international joint conference on artificial intelligence (IJCAI) (pp. 1022-1029). Chambéry, France: Morgan Kaufmann.
9. Files, C (2000). A new functional decomposition method as applied to machine learning and VLSI layout. Department of Electrical and Computer Engineering, Portland State University.
10. Friedman S.J., Supowit K.J. Finding the optimal variable ordering for binary decision diagrams. IEEE Transactions on Computers. 39:(5):1990;710-713.
11. Fujita M., Matsunaga Y., Kakuda T. On variable ordering of binary decision diagrams for the application of multi-level logic synthesis. Proceedings of the European Design Automation Conference. 1991;50-54.
12. Gansner E.R., Koutsofios E., North S.C., Vo K.P. A technique for drawing directed graphs. IEEE Transactions on Software Engineering. 19:(3):1993;214-230.
13. Henley W.E., Hand D.J. Construction of a k-nearest neighbour credit-scoring system. IMA Journal of Mathematics Applied in Business and Industry. 8:1997;305-321.
14. Horiyama T., Ibaraki T. Ordered binary decision diagrams as knowledge-bases. Artificial Intelligence. 136:(2):2002;189-213.
15. Kam T., Villa T., Brayton R.K., Sangiovanni-Vincentelli A.L. Multi-valued decision diagrams: theory and applications. International Journal on Multiple-Valued Logic. 4:(1/2):1998;9-62.
16. Kohavi, R (1996). Wrappers for performance enhancement and oblivious decision graphs. Department of Computer Science, Stanford University.
17. Kohavi, R., & Li, C. H. (1995). Oblivious decision trees, graphs, and top-down pruning. Proceedings of the fourteenth international joint conference on artificial intelligence (pp. 1071-1077). Montréal, Que., Canada: Morgan Kaufmann Publishers.
18. Oliveira A.L., Sangiovanni-Vincentelli A.L. Using the minimum description length principle to infer reduced ordered decision graphs. Machine Learning. 25:(1):1996;23-50.
19. Quinlan J.R. C4.5 programs for machine learning. 1993;Morgan Kaufmann, Montréal, Que., Canada.
20. Setiono R., Liu H. Symbolic representation of neural networks. IEEE Computer. 29:(3):1996;71-77.
21. Steenackers A., Goovaerts M.J. A credit scoring model for personal loans. Insurance: Mathematics and Economics. 8:1989;31-34.
22. Thomas L.C. A survey of credit and behavioural scoring: forecasting financial risk of lending to customers. International Journal of Forecasting. 16:2000;149-172.