WBCsvm: Weighted Bayesian Classification based on Support Vector Machines
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A decision tree-based attribute weighting filter for naive Bayes
Knowledge-Based Systems
One dependence augmented naive bayes
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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Naive Bayes is a effective and widely used data mining algorithm for classification, but its unrealistic attribute conditional independence harm its performance. Selecting attributes subsets is an important approach to extend the Naive Bayes, and the state-of-the-art SBC algorithm has better accuracy in classification. In this paper, we review the weighted attribute method for Naive Bayes, and explain SBC is one of the special case in weighted attributed methods. Interesting this method, we present a new one dependence augmented Naive Bayes with weighted attribute called WODANB, which use the fuzzy Support Vector Machine to optimize the weights. Experiment on whole 36 datasets recommended by Weka, results show that WODANB significant outperforms than NB, SBC, ODANB, TAN.