Learning Complex Conditional Probabilities from Data

Naive Bayes is a popular machine learning technique due to its efficiency, direct theoretical foundation and strong classification performance. Our techniques seek to strengthen its accuracy by overcoming the deficiencies of its attribute independence assumption. Averaged One Dependence Estimators (AODE) provides particularly high prediction accuracy with relatively modest computational overheads. Lazy Bayesian Rules (LBR) provides very high prediction accuracy for large training sets, and is computationally efficient when few objects are to be classified for each training set. [AODE, LBR and related papers]