Learning Bayesian Network Classifiers for Credit Scoring

Abstract

In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for financial credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using a global score metric. For comparison, two of statistical classifiers will be evaluated. The experiments will be carried out on three real life credit scoring data sets. It will be shown that general Bayesian network classifiers learned by a global score search have a good performance and by using the Markov blanket concept, a natural form of input selection is obtained, which results in parsimonious and powerful models for financial credit scoring.