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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Extended Tree Augmented Naive Classifier
verfasst von : Cassio P. de Campos, Marco Cuccu, Giorgio Corani, Marco Zaffalon
Erschienen in: Probabilistic Graphical Models
Verlag: Springer International Publishing
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This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.