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Confidence intervals for probabilistic network classifiers

Egmont-Petersen, M., Feelders, A. and Baesens, B. (2005) Confidence intervals for probabilistic network classifiers Computational Statistics & Data Analysis, 49, (4), pp. 998-1019. (doi:10.1016/j.csda.2004.06.018).

Record type: Article

Abstract

Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the feature variables are discrete. It is argued that their white-box character makes them transparent, a requirement in various applications such as, e.g., credit scoring. In addition, the exact error rate of a probabilistic network classifier can be computed without a dataset. First, the exact error rate for probabilistic network classifiers is specified. Secondly, the exact sampling distribution for the conditional probability estimates in a probabilistic network classifier is derived. Each conditional probability is distributed according to the bivariate binomial distribution. Subsequently, an approach for computing the sampling distribution and hence confidence intervals for the posterior probability in a probabilistic network classifier is derived. Our approach results in parametric bootstrap confidence intervals. Experiments with general probabilistic network classifiers, the Naive Bayes classifier and tree augmented Naive Bayes classifiers (TANs) show that our approximation performs well. Also simulations performed with the Alarm network show good results for large training sets. The amount of computation required is exponential in the number of feature variables. For medium and large-scale classification problems, our approach is well suited for quick simulations.Arunning example from the domain of credit scoring illustrates how to actually compute the sampling distribution of the posterior probability.

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Published date: 2005
Organisations: Management

Identifiers

Local EPrints ID: 36735
URI: http://eprints.soton.ac.uk/id/eprint/36735
ISSN: 0167-9473
PURE UUID: c8ce3fdd-e120-4f3f-a76d-5f7537e15ed9

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Date deposited: 11 Jul 2006
Last modified: 17 Jul 2017 15:44

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Contributors

Author: M. Egmont-Petersen
Author: A. Feelders
Author: B. Baesens

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