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Improving the Performance of Bayesian Belief Network Classifiers via Decision Tree Based Feature Selection. January 2010. January 2010. Source; DBLP.
In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers - Nai've-Bayes, tree augmented Nai've-Bayes, ...
Abstract. This paper investigates the methods for learning predictive classifiers based on Bayesian belief networks (BN) -- primarily unrestricted Bayesian ...
to improved performance of the Naïve Bayesian Classifier, especially in the ... unseen data, comparing with the accuracy based on unpruned decision trees.
Missing: Belief Network
and compares the performance of Bayesian networks and decision trees at two levels: the activity ... between the Bayesian network and the CHAID decision tree ...
Missing: via Feature
In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning. Bayesian networks. These networks are factored ...
On using Bayesian networks for complexity reduction in decision trees · A ... Learning belief networks from data: an information theory based approach.
improve the classification performance. In the next section, we introduce BAN, a novel discriminative structure learning algorithm. V. STRUCTURE LEARNING.
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of the network. Then the learning process uses only the selected features as nodes in learning the. Bayesian network. Our goal is to construct net-.
Abstract. A new method for Feature Subset Selection in machine learning, FSS-EBNA (Feature Subset Se- lection by Estimation of Bayesian Network Algorithm), ...
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