Confidence intervals for probabilistic network classifiers
Egmont-Petersen, M., Feelders, A. and Baesens, B. (2005) Confidence intervals for probabilistic network classifiers. Computational Statistics and Data Analysis, 49, (4), 998-1019. (doi:10.1016/j.csda.2004.06.018).
Full text not available from this repository.
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.
|Digital Object Identifier (DOI):||doi:10.1016/j.csda.2004.06.018|
|Subjects:||H Social Sciences > HG Finance
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
H Social Sciences > HA Statistics
|Divisions:||University Structure - Pre August 2011 > School of Management
|Date Deposited:||11 Jul 2006|
|Last Modified:||06 Aug 2015 02:32|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)