Learning attribute weighted AODE for ROC area ranking

One related area that has received little attention with regards to AODE is the use of attribute weights for ranking. This paper investigates how to learn an AWAODE with accurate ranking from data sets. We first explore various methods, such as gain ratio, correlation-based feature selection attribute selection algorithm, mutual information and relief attribute ranking algorithm. Our experiments clearly show that an attribute weighted AODE trained to produce AUC ranking outperforms AODE and NB. Then, we propose a new approach to weight AODE for generating accurate ranking, called decision tree-based attribute weighted averaged one-dependence estimator, simply DTWAODE. In DTWAODE, the weight for an attribute is set according to its depth in the decision tree building on the training samples. The experimental results show that our new attribute weighted model via AODE performance effectively than AODE and other attribute weighted approaches on AUC.