Not so naive Bayes: aggregating one-dependence estimators
Machine Learning
An Extendable Meta-learning Algorithm for Ontology Mapping
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
The Knowledge Engineering Review
NB+: An improved Naïve Bayesian algorithm
Knowledge-Based Systems
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The naive Bayes classifier is widely used in interactiveapplications due to its computational efficiency, direct theoreticalbase, and competitive accuracy. However, its attributeindependence assumption can result in sub-optimalaccuracy. A number of techniques have explored simple relaxationsof the attribute independence assumption in or-derto increase accuracy. Among these, the lazy Bayesianrule () and the tree-augmented naive Bayes ()have demonstrated strong prediction accuracy. However,their relative performance has never been evaluated. Thispaper compares and contrasts these two techniques, findingthat they have comparable accuracy and hence shouldbe selected according to computational profile. LBR is desirablewhen small numbers of objects are to be classifiedwhile TAN is desirable when large numbers of objects areto be classified.