Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Generalized Dirichlet distribution in Bayesian analysis
Applied Mathematics and Computation
On Estimating Probabilities in Tree Pruning
EWSL '91 Proceedings of the European Working Session on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Alternative prior assumptions for improving the performance of naïve Bayesian classifiers
Data Mining and Knowledge Discovery
Bayesian network classifiers versus selective k-NN classifier
Pattern Recognition
A hybrid discretization method for naïve Bayesian classifiers
Pattern Recognition
Journal of Intelligent and Robotic Systems
Speeding up incremental wrapper feature subset selection with Naive Bayes classifier
Knowledge-Based Systems
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The generalized Dirichlet distribution has been shown to be a more appropriate prior for naive Bayesian classifiers, because it can release both the negative-correlation and the equal-confidence requirements of the Dirichlet distribution. The previous research did not take the impact of individual attributes on classification accuracy into account, and therefore assumed that all attributes follow the same generalized Dirichlet prior. In this study, the selective naive Bayes mechanism is employed to choose and rank attributes, and two methods are then proposed to search for the best prior of each single attribute according to the attribute ranks. The experimental results on 18 data sets show that the best approach is to use selective naive Bayes for filtering and ranking attributes when all of them have Dirichlet priors with Laplace's estimate. After the ranks of the chosen attributes are determined, individual setting is performed to search for the best noninformative generalized Dirichlet prior for each attribute. The selective naive Bayes is also compared with two representative filters for the feature selection, and the experimental results show that it has the best performance.