Selecting One Dependency Estimators in Bayesian Network Using Different MDL Scores and Overfitting Criterion

The Averaged One Dependency Estimator (AODE) is integrated all possible Super-Parent-One-Dependency Estimators (SPODEs) and estimates class conditional probabilities by averaging them. In an AODE network some redundant SPODEs maybe result in some bias of classifiers, as a consequence, it could reduce the classification accuracy substantially. In this paper, a kind of MDL metrics is used to select SPODEs in a whole or partially, therefore there are three different classifiers presented. The performance comparisons between them and AODE have been shown not only the theoretical analyses are reasonable, but also efficient and effective. And Mean Square Error (MSE) is used to test overfitting. Experiential results have indicated that the classifier using MDL score metrics had better performance than original AODE, and at the same time, has less overfitting. At the end of the paper, further discussions and verifications of some properties of overfitting have also shown in the experiments.