Machine Learning - Special issue on learning with probabilistic representations
Survey of Improving Naive Bayes for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Random one-dependence estimators
Pattern Recognition Letters
One Dependence Value Difference Metric
Knowledge-Based Systems
Improving Tree augmented Naive Bayes for class probability estimation
Knowledge-Based Systems
Improved algorithms for weighted and unweighted set splitting problems
COCOON'07 Proceedings of the 13th annual international conference on Computing and Combinatorics
Information Sciences: an International Journal
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Data mining applications require learning algorithms to have high predictive accuracy, scale up to large datasets, and produce comprehensible outcomes. Naive Bayes classifier has received extensive attention due to its efficiency, reasonable predictive accuracy, and simplicity. However, the assumption of attribute dependency given class of Naive Bayes is often violated, producing incorrect probability that can affect the success of data mining applications. We extend Naive Bayes classifier to allow certain dependency relations among attributes. Comparing to previous extensions of Naive Bayes, our algorithm is more efficient (more so in problems with a large number of attributes), and produces simpler dependency relation for better comprehensibility, while maintaining very similar predictive accuracy.