Scaling up the accuracy of Bayesian classifier based on frequent itemsets by m-estimate
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
One Dependence Value Difference Metric
Knowledge-Based Systems
Bayesian classifiers for positive unlabeled learning
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Improving Tree augmented Naive Bayes for class probability estimation
Knowledge-Based Systems
Ensemble learning based on multi-task class labels
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Double-layer bayesian classifier ensembles based on frequent itemsets
International Journal of Automation and Computing
A Modified Short and Fukunaga Metric based on the attribute independence assumption
Pattern Recognition Letters
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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Because learning an optimal Bayesian network classifier is an NP-hard problem, learning-improved naive Bayes has attracted much attention from researchers. In this paper, we summarize the existing improved algorithms and propose a novel Bayes model: hidden naive Bayes (HNB). In HNB, a hidden parent is created for each attribute which combines the influences from all other attributes. We experimentally test HNB in terms of classification accuracy, using the 36 UCI data sets selected by Weka, and compare it to naive Bayes (NB), selective Bayesian classifiers (SBC), naive Bayes tree (NBTree), tree-augmented naive Bayes (TAN), and averaged one-dependence estimators (AODE). The experimental results show that HNB significantly outperforms NB, SBC, NBTree, TAN, and AODE. In many data mining applications, an accurate class probability estimation and ranking are also desirable. We study the class probability estimation and ranking performance, measured by conditional log likelihood (CLL) and the area under the ROC curve (AUC), respectively, of naive Bayes and its improved models, such as SBC, NBTree, TAN, and AODE, and then compare HNB to them in terms of CLL and AUC. Our experiments show that HNB also significantly outperforms all of them.