Semi-naive Exploitation of One-Dependence Estimators

Abstract

Abstract-It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, and the exploration of low orders for ensuring a stable probability estimate from limited training samples. By allowing one-order attribute dependencies, one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies whilst keeping the effectiveness of probability estimation, and therefore leading to excellent performance. In previous studies, however, ODEs were exploited in simple ways, such as by averaging, for classification. In this paper, we propose a semi-naive exploitation of ODEs that fits a function of ODEs to pursue higher-order attribute dependencies. Extensive experiments show that the proposed SNODE approach can achieve better performance than many state-of-the-art Bayesian classifiers.

Efficient learning ensemble SuperParent-one-dependence estimator by maximizing conditional log likelihood

“…NB has been extensively used in document classification, spam detection, and intrusion detection (Baig, Shaheen, & AbdelAal, 2011a;Li, Yu, & Zhou, 2009). However, lack of important dependence information sometimes hampers classification performance significantly (Flores, Gámez, & Martínez, 2014).…”

Random one-dependence estimators

“…In HNB, a hidden parent node is created for each attribute node to integrate the influences from all the other attribute nodes. Li et al (2009) proposed a semi-naive exploitation of ODEs. They simply denote it SNODE in their paper.…”

Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies

“…However, the dependency relationships between attributes always violate this assumption in many learning tasks. Many methods [20]- [27] attempt to improve the classification performance of NB by relaxing its independence assumption, such as TAN.…”