### Discrete Bayesian Network Classifiers: A SurveyDiscrete Bayesian Network Classifiers: A Survey

Access Restriction
Subscribed

 Author Bielza, Concha ♦ Larraaga, Pedro Source ACM Digital Library Content type Text Publisher Association for Computing Machinery (ACM) File Format PDF Copyright Year ©2014 Language English
 Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science Subject Keyword Bayesian multinets ♦ Bayesian network ♦ Markov blanket ♦ Supervised classification ♦ Feature subset selection ♦ Generative and discriminative classifiers ♦ Naive Bayes Abstract 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, these classifiers have many strengths, like model interpretability, accommodation to complex data and classification problem settings, existence of efficient algorithms for learning and classification tasks, and successful applicability in real-world problems. In this article, we survey the whole set of discrete Bayesian network classifiers devised to date, organized in increasing order of structure complexity: naive Bayes, selective naive Bayes, seminaive Bayes, one-dependence Bayesian classifiers, $\textit{k}-dependence$ Bayesian classifiers, Bayesian network-augmented naive Bayes, Markov blanket-based Bayesian classifier, unrestricted Bayesian classifiers, and Bayesian multinets. Issues of feature subset selection and generative and discriminative structure and parameter learning are also covered. ISSN 03600300 Age Range 18 to 22 years ♦ above 22 year Educational Use Research Education Level UG and PG Learning Resource Type Article Publisher Date 2014-07-01 Publisher Place New York e-ISSN 15577341 Journal ACM Computing Surveys (CSUR) Volume Number 47 Issue Number 1 Page Count 43 Starting Page 1 Ending Page 43

#### Open content in new tab

Source: ACM Digital Library