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Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification

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dc.contributor.authorAng, Sau Loong
dc.date.accessioned2024-09-19T03:26:24Z
dc.date.available2024-09-19T03:26:24Z
dc.date.issued2019-03
dc.description.abstractNaive Bayes (NB) is an efficient Bayesian classifier with wide range of applications in data classification. Having the advantage with its simple structure. Naive Bayes gains attention among the researchers with its good accuracy in classification result. Nevertheless, the major drawback of Naive Bayes is the strong independence assumption among the features which is restrictive. This weakness causes not only confusion in the causal relationships among the features but also doubtful representation of the real structure of Bayesian Network for classification. Further development of Naive Bayes in augmenting extra links or dependent relationships between the features such as the Tree Augmented Naive Bayes (TAN) end up with slight improvement in accuracy of classification result where the main problems stated above remain unsolved.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/20496
dc.language.isoen
dc.subjectBayesian Networks
dc.subjectData Classification
dc.titleBayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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