On estimating probabilities in tree pruning
EWSL-91 Proceedings of the European working session on learning on Machine learning
Generalized Dirichlet distribution in Bayesian analysis
Applied Mathematics and Computation
Machine Learning
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A comparison of event models for Naive Bayes anti-spam e-mail filtering
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Some Effective Techniques for Naive Bayes Text Classification
IEEE Transactions on Knowledge and Data Engineering
Raising the baseline for high-precision text classifiers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Alternative prior assumptions for improving the performance of naïve Bayesian classifiers
Data Mining and Knowledge Discovery
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Computational Statistics & Data Analysis
A hybrid discretization method for naïve Bayesian classifiers
Pattern Recognition
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The generalized Dirichlet distribution has been shown to be a more appropriate prior than the Dirichlet distribution for naïve Bayesian classifiers. When the dimension of a generalized Dirichlet random vector is large, the computational effort for calculating the expected value of a random variable can be high. In document classification, the number of distinct words that is the dimension of a prior for naïve Bayesian classifiers is generally more than ten thousand. Generalized Dirichlet priors can therefore be inapplicable for document classification from the viewpoint of computational efficiency. In this paper, some properties of the generalized Dirichlet distribution are established to accelerate the calculation of the expected values of random variables. Those properties are then used to construct noninformative generalized Dirichlet priors for naïve Bayesian classifiers with multinomial models. Our experimental results on two document sets show that generalized Dirichlet priors can achieve a significantly higher prediction accuracy and that the computational efficiency of naïve Bayesian classifiers is preserved.