Abstract
Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, semi-naive Bayesian classifiers which utilize one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies; meanwhile, the probability estimation in ODEs is effective, thus leading to excellent performance. In previous studies, ODEs were exploited directly in a simple way. For example, averaged one-dependence estimators (AODE) weaken the attribute independence assumption by directly averaging all of a constrained class of classifiers. However, all one-dependence estimators in AODE have the same weights and are treated equally. In this study, we propose a new paradigm based on a simple, efficient, and effective attribute value weighting approach, called attribute value weighted average of one-dependence estimators (AVWAODE). AVWAODE assigns discriminative weights to different ODEs by computing the correlation between the different root attribute value and the class. Our approach uses two different attribute value weighting measures: the Kullback–Leibler (KL) measure and the information gain (IG) measure, and thus two different versions are created, which are simply denoted by AVWAODE-KL and AVWAODE-IG, respectively. We experimentally tested them using a collection of 36 University of California at Irvine (UCI) datasets and found that they both achieved better performance than some other state-of-the-art Bayesian classifiers used for comparison.
Highlights
A Bayesian network (BN) is a graphical model that encodes probabilistic relationships among all variables, where nodes represent attributes, edges represent the relationships between the attributes, and directed arcs can be used to explicitly represent the joint probability distribution
We propose a new paradigm based on a simple, efficient, and effective attribute value weighting approach, called attribute value weighted average of one-dependence estimators (AVWAODE)
Our AVWAODE approach is well balanced between the ground-truth dependencies approximation and the effectiveness of probability estimation
Summary
A Bayesian network (BN) is a graphical model that encodes probabilistic relationships among all variables, where nodes represent attributes, edges represent the relationships between the attributes, and directed arcs can be used to explicitly represent the joint probability distribution. Among the numerous Bayesian learning approaches, semi-naive Bayesian classifiers which utilize one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies; the probability estimation in ODEs is effective, leading to excellent performance. It will be interesting to study whether a better performance can be achieved by combining attribute value weighting with the ODEs. The resulting model which combines attribute value weighting with the ODEs inherits the effectiveness of ODEs; this approach is a new paradigm of weighting approach in classification learning. We assume that the significance of each ODE can be decomposed, and in the structure of highly predictive ODE, the different root attribute value should be strongly associated with the class Based on these assumptions, we assign a different weight to each ODE by computing the correlation between the root attribute value and the class.
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