%0 Journal Article %1 WebbBoughtonWang05 %A Webb, G. I. %A Boughton, J. %A Wang, Z. %C Netherlands %D 2005 %I Springer %J Machine Learning %K Conditional Estimation,AODE Probability %N 1 %P 5-24 %T Not So Naive Bayes: Aggregating One-Dependence Estimators %V 58 %X Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and TAN have demonstrated remarkable error performance. However, both techniques obtain this outcome at a considerable computational cost. We present a new approach to weakening the attribute independence assumption by averaging all of a constrained class of classifiers. In extensive experiments this technique delivers comparable prediction accuracy to LBR and TAN with substantially improved computational efficiency.

@article{WebbBoughtonWang05, abstract = {Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and TAN have demonstrated remarkable error performance. However, both techniques obtain this outcome at a considerable computational cost. We present a new approach to weakening the attribute independence assumption by averaging all of a constrained class of classifiers. In extensive experiments this technique delivers comparable prediction accuracy to LBR and TAN with substantially improved computational efficiency.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Netherlands}, audit-trail = {3/5/04 Pre-print posted}, author = {Webb, G. I. and Boughton, J. and Wang, Z.}, biburl = {https://www.bibsonomy.org/bibtex/2ed205239795d8c14b847aa74c7c6bede/giwebb}, interhash = {325cd34be85d1cefe7db6f69cdaeee31}, intrahash = {ed205239795d8c14b847aa74c7c6bede}, journal = {Machine Learning}, keywords = {Conditional Estimation,AODE Probability}, number = 1, pages = {5-24}, publisher = {Springer}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Not So Naive Bayes: Aggregating One-Dependence Estimators}, volume = 58, year = 2005 }