Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification
Naive 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...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://eprints.usm.my/61159/1/Bayesian%20Networks%20with%20greedy%20cut.pdf http://eprints.usm.my/61159/ |
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| Summary: | Naive 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. |
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