B. Baesens, M. Egmont-Petersen,
R. Castelo, J. Vanthienen. "Learning Bayesian network classifiers for
credit scoring using Markov Chain Monte Carlo search," Technical report
UU-CS-2001-58, Institute of Computer and Information Sciences, Utrecht
In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search. The experiments will be carried out on three real life credit scoring data sets. It will be shown that MCMC Bayesian network classifiers have a very good performance and by using the Markov Blanket concept, a natural form of feature selection is obtained, which results in parsimonious and powerful models for financial credit scoring.
Electronic reprint , or contact me: michael * egmont-petersen.nl (with * indicating @)