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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/576824
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dc.contributor.authorSau Loong Ang (USM)
dc.contributor.authorHong Choon Ong (USM)
dc.contributor.authorHeng Chin Low (USM)
dc.date.accessioned2023-11-06T02:08:14Z-
dc.date.available2023-11-06T02:08:14Z-
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:82495
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/576824-
dc.descriptionNaive Bayes (NB) is a simple but powerful tool for data classification. It is widely used in classification due to the simplicity of its structure and its capability to produce surprisingly good results for classification. However, the independence assumption among the features is not practical in real datasets. Attempts have been made to improve the Naive Bayes by introducing links or dependent relationships between the features such as the Tree Augmented Naive Bayes (TAN). In this study, we show the accuracy of a General Bayesian Network (GBN) used with the Hill-Climbing learning method, which does not impose any restrictions on the structure and better represents the dataset. We also show that it gives equivalent performances or even outperforms Naive Bayes and TAN in most of the data classification.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-24-1-1
dc.subjectNaive Bayes
dc.subjectClassification
dc.subjectTree Augmented Naive Bayes
dc.subjectGeneral Bayesian Network
dc.titleClassification using the general bayesian network
dc.typeJournal Article
dc.format.volume24
dc.format.pages205-211
dc.format.issue1
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