Discrete Bayesian network classifiers: a survey

Bielza Lozoya, María Concepción ORCID: https://orcid.org/0000-0001-7109-2668 and Larrañaga Múgica, Pedro María ORCID: https://orcid.org/0000-0002-1885-4501 (2014). Discrete Bayesian network classifiers: a survey. "ACM Computing Surveys", v. 47 (n. 1); pp. 1-43. ISSN 1557-7341. https://doi.org/doi.org/10.1145/2576868.


Title: Discrete Bayesian network classifiers: a survey
Item Type: Article
Título de Revista/Publicación: ACM Computing Surveys
Date: July 2014
ISSN: 1557-7341
Volume: 47
Freetext Keywords: Algorithms, Design, Performance
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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, 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

Funding Projects

Government of Spain
TIN2010- 20900-C04-04
Government of Spain
Cajal Blue Brain

More information

Item ID: 72762
DC Identifier: https://oa.upm.es/72762/
OAI Identifier: oai:oa.upm.es:72762
DOI: doi.org/10.1145/2576868
Official URL: https://dl.acm.org/doi/10.1145/2576868
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 02 Mar 2023 13:43
Last Modified: 02 Mar 2023 13:43
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