Simple Bayesian Classifier Applied to Learning
| Autor: | Byron Oviedo, Cristian Zambrano-Vega, Alina Martinez, Joffre León-Acurio |
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| Rok vydání: | 2018 |
| Předmět: |
Markov chain
Computer science business.industry Bayesian probability Boundary (topology) 02 engineering and technology Machine learning computer.software_genre Naive Bayes classifier Software Simple (abstract algebra) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Akaike information criterion business Class variable computer |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783030055318 |
| DOI: | 10.1007/978-3-030-05532-5_29 |
| Popis: | In this article, we propose the use of a new simple Bayesian classifier (SBND) that quickly learns a Markov boundary of the class variable and a network structure relating class variables and the said boundary. This model is compared with other Bayesian classifiers, then experimental tests are carried out for which 31 well-known ICU databases and two bases of artificial variables have been used. With these databases we compare the results obtained by such algorithms studied in the state of the art such as Naive Bayes, TAN, BAN, RPDag, CRPDag, SBND and combinations with different metrics such as K2, BIC, Akaike, BDEu. The experimental work was done in Elvira software. |
| Databáze: | OpenAIRE |
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