Inproceedings,
Comparison of Lazy Bayesian Rule Learning and Tree-Augmented Bayesian Learning
Proceedings of the IEEE International Conference on Data Mining (ICDM-2002), page 775-778. Los Alamitos, CA, IEEE Computer Society, (2002)
Abstract
The na?ve 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, Lazy Bayesian Rules (LBR) and Tree-Augmented Na?ve-Bayes (TAN) have demonstrated strong prediction accuracy. However, their relative performance has never been evaluated. This 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
