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Learning the tree augmented naive bayes classifier from incomplete datasets.
| Content Provider | CiteSeerX |
|---|---|
| Author | François, Olivier C. H. |
| Abstract | The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid or diagnosis, in particular thanks to its inference capabilities, even when data are incomplete. For classification tasks, Naive Bayes and Augmented Naive Bayes classifiers have shown excellent performances. Learning a Naive Bayes classifier from incomplete datasets is not difficult as only parameter learning has to be performed. But there are not many methods to efficiently learn Tree Augmented Naive Bayes classifiers from incomplete datasets. In this paper, we take up the structural em algorithm principle introduced by (Friedman, 1997) to propose an algorithm to answer this question. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Parameter Learning Incomplete Datasets Decision Aid Many Method Classification Task Structural Em Algorithm Principle Naive Bayes Classifier Particular Thanks Naive Bayes Bayesian Network Formalism Inference Capability Augmented Naive Bayes Classifier Tree Augmented Naive Bayes Classifier Many Area Excellent Performance |
| Content Type | Text |
