A decision support tool for credit domains: Bayesian network with a variable selector based on imprecise probabilities

Abstract

A Bayesian Network (BN) is a graphical structure, with associated conditional probability tables. This structure allows us to obtain different knowledge than the one obtained from standard classifiers. With a BN, representing a dataset, we can calculate different probabilities about a set of features with respect to other ones. This inference can be more powerful than the one obtained from classifiers. A BN can be built from data and have analytical and diagnostic capabilities that make it very suitable for credit domains. Credit scoring and risk analysis are fundamental tasks for financial institutions with the aim to avoid important losses. In these tasks and other domains, an excessive number of features can convert a BN into a complex and difficult to interpret model, but a few number of features can represent a loss of information obtained from data. A new method based on imprecise probabilities is presented to select an informative subset of features. Using this new feature selection method, we can build a BN that has an excellent adjustment to the data, considering a reduced number of features. Via a set of experiments, it is shown that the adjustment is better than the ones obtained with no previous variable selection method and with a similar and successful variable subset selection method based on precise probabilities. Finally, a BN is built with two important characteristics: (i) it represents a better adjustment to the data; and (ii) it has a low complexity (better interpretability) due to the small number of important selected features. A practical example about inference on a BN to help on credit risk analysis is also presented.