We have had to wait over 30 years since the naive Bayes model was first introduced in 1960
for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks …

Ensemble pruning deals with the reduction of an ensemble of predictive models in order to
improve its efficiency and predictive performance. The last 12 years a large number of …

Ensemble methods have been successfully used as a classification scheme. The reduction
of the complexity of this popular learning paradigm motivated the appearance of ensemble …

Machine Learning for Data Science Handbook Lior Rokach Oded Maimon Erez Shmueli Editors
Machine Learning for Data Science Handbook Data Mining and Knowledge Discovery …

Bayesian network classifiers remain of great interest in recent years, among which semi-
naive Bayesian classifiers which utilize superparent one-dependence estimators (SPODEs) …

Abstract SuperParent-One-Dependence Estimators (SPODEs) represent a family of semi-
naive Bayesian classifiers which relax the attribute independence assumption of Naive …

The increase in data variability and quantity makes it urgent for learning complex
multivariate probability distributions. The state-of-the-art Tree Augmented Naive Bayes …

Abstract Learning Bayesian network classifiers (BNCs) from data is NP-hard. Of numerous
BNCs, averaged one-dependence estimator (AODE) performs extremely well against more …

Ever since the success of naive Bayes (NB) in achieving excellent classification
performance and the least computational overhead, more and more researchers have …

Semi-naive Bayesian classifiers seek to retain the numerous strengths of naive Bayes while
reducing error by relaxing the attribute independence assumption. Backwards Sequential …