Weighted average of one-dependence estimators†

Abstract

Naive Bayes (NB) is a probability-based classification model which is based on the attribute independence assumption. However, in many real-world data mining applications, its attribute independence assumption is often violated. Responding to this fact, researchers have made a substantial amount of effort to improve the classification accuracy of NB by weakening its attribute independence assumption. For a recent example, averaged one-dependence estimators (AODE) is proposed, which weakens its attribute independence assumption by averaging all models from a restricted class of one-dependence classifiers. However, all one-dependence classifiers in AODE have same weights and are treated equally. According to our observation, different one-dependence classifiers should have different weights. Therefore, in this article, we proposed an improved model called weighted average of one-dependence estimators (WAODE) by assigning different weights to these one-dependence classifiers. In our WAODE, four different weighting approaches are designed and thus four different versions are created. For simplicity, we respectively denote them by WAODE-MI, WAODE-ACC, WAODE-CLL and WAODE-AUC. The experimental results on a large number of UCI datasets published on the main website of Weka platform show that our WAODE significantly outperform AODE.