Comparison of lazy Bayesian rule, and tree-augmented Bayesian learning

The naive Bayes classifier is widely used in interactive applications due to its computational efficiency, direct theoretical base, and competitive accuracy. However its attribute independence assumption can result in sub-optimal accuracy. A number of techniques have explored simple relaxations of the attribute independence assumption in order to increase accuracy. Among these, the lazy Bayesian rule (LBR) and the tree-augmented naive Bayes (TAN) have demonstrated strong prediction accuracy. However their relative performance has never been evaluated. The paper compares and contrasts these two techniques, finding that they have comparable accuracy and hence should be selected according to computational profile. LBR is desirable when small numbers of objects are to be classified while TAN is desirable when large numbers of objects are to be classified.