Machine Learning - Special issue on learning with probabilistic representations
An Improved Learning Algorithm for Augmented Naive Bayes
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Toward Bayesian Classifiers with Accurate Probabilities
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Inference for the Generalization Error
Machine Learning
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Discriminative parameter learning for Bayesian networks
Proceedings of the 25th international conference on Machine learning
Learning decision tree for ranking
Knowledge and Information Systems
Structure identification of Bayesian classifiers based on GMDH
Knowledge-Based Systems
A Novel Bayes Model: Hidden Naive Bayes
IEEE Transactions on Knowledge and Data Engineering
On the classification performance of TAN and general Bayesian networks
Knowledge-Based Systems
Discriminative model selection for belief net structures
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Weightily averaged one-dependence estimators
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
NB+: An improved Naïve Bayesian algorithm
Knowledge-Based Systems
Learning naive bayes for probability estimation by feature selection
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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Numerous algorithms have been proposed to improve Naive Bayes (NB) by weakening its conditional attribute independence assumption, among which Tree Augmented Naive Bayes (TAN) has demonstrated remarkable classification performance in terms of classification accuracy or error rate, while maintaining efficiency and simplicity. In many real-world applications, however, classification accuracy or error rate is not enough. For example, in direct marketing, we often need to deploy different promotion strategies to customers with different likelihood (class probability) of buying some products. Thus, accurate class probability estimation is often required to make optimal decisions. In this paper, we investigate the class probability estimation performance of TAN in terms of conditional log likelihood (CLL) and present a new algorithm to improve its class probability estimation performance by the spanning TAN classifiers. We call our improved algorithm Averaged Tree Augmented Naive Bayes (ATAN). The experimental results on a large number of UCI datasets published on the main web site of Weka platform show that ATAN significantly outperforms TAN and all the other algorithms used to compare in terms of CLL.