Machine Learning - Special issue on learning with probabilistic representations
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Lazy Learning of Bayesian Rules
Machine Learning
Semi-Naive Bayesian Classifier
EWSL '91 Proceedings of the European Working Session on Machine Learning
Induction of Recursive Bayesian Classifiers
ECML '93 Proceedings of the European Conference on Machine Learning
Adjusted Probability Naive Bayesian Induction
AI '98 Selected papers from the 11th Australian Joint Conference on Artificial Intelligence on Advanced Topics in Artificial Intelligence
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Classification using Hierarchical Naïve Bayes models
Machine Learning
Efficient lazy elimination for averaged one-dependence estimators
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
To select or to weigh: a comparative study of model selection and model weighing for SPODE ensembles
ECML'06 Proceedings of the 17th European conference on Machine Learning
Robust bayesian linear classifier ensembles
ECML'05 Proceedings of the 16th European conference on Machine Learning
| Hi-index | 0.00 |
Averaged One-Dependence Estimators (AODE) classifies by uniformly aggregating all qualified one-dependence estimators (ODEs). Its capacity to significantly improve naive Bayes' accuracy without undue time complexity has attracted substantial interest. Forward Sequential Selection and Backwards Sequential Elimination are effective wrapper techniques to identify and repair harmful interdependencies which have been profitably applied to naive Bayes. However, their straightforward application to AODE has previously proved ineffective. We investigate novel variants of these strategies. Our extensive experiments show that elimination of child attributes from within the constituent ODEs results in a significant improvement in probability estimate and reductions in bias and error relative to unmodified AODE. In contrast, elimination of complete constituent ODEs and the four types of attribute addition are found to be less effective and do not demonstrate any strong advantage over AODE. These surprising results lead to effective techniques for improving AODE's prediction accuracy.