Active shape models—their training and application
Computer Vision and Image Understanding
Machine Learning - Special issue on learning with probabilistic representations
Introduction to Bayesian Networks
Introduction to Bayesian Networks
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
A Tight Upper Bound on the Bayesian Probability of Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Error-Bars for Belief Net Inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Learning Bayesian Network Classifiers for Credit Scoring Using Markov Chain Monte Carlo Search
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Intelligent data analysis
Paper: On the quality of neural net classifiers
Artificial Intelligence in Medicine
IEEE Transactions on Image Processing
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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. A running example from the domain of credit scoring illustrates how to actually compute the sampling distribution of the posterior probability.