Credit scoring, decision tables, rule extraction, neural networks Abstract: Accuracy and comprehensibility are two important criteria when developing decision support systems for credit scoring. In this paper, we focus on the second criterion and propose the use of decision tables as an alternative knowledge visualisation formalism which lends itself very well to building intelligent and userfriendly credit scoring systems. Starting from a set of propositional if-then rules extracted by a neural network rule extraction algorithm, we construct decision tables and demonstrate their efficiency and user-friendliness for two real-life credit scoring cases.
CITATION STYLE
Baesens, B., Mues, C., De Backer, M., Vanthienen, J., & Setiono, R. (2003). Building intelligent credit scoring systems using decision tables. In ICEIS 2003 - Proceedings of the 5th International Conference on Enterprise Information Systems (Vol. 2, pp. 19–25). Escola Superior de Tecnologia do Instituto Politecnico de Setubal. https://doi.org/10.1007/1-4020-2673-0_15
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