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...

Full description

Bibliographic Details
Main Author: Ang, Sau Loong
Format: Thesis
Language:English
Published: 2019
Subjects:
Online Access:http://eprints.usm.my/61159/
http://eprints.usm.my/61159/1/Bayesian%20Networks%20with%20greedy%20cut.pdf
_version_ 1848884634813726720
author Ang, Sau Loong
author_facet Ang, Sau Loong
author_sort Ang, Sau Loong
building USM Institutional Repository
collection Online Access
description 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.
first_indexed 2025-11-15T19:09:50Z
format Thesis
id usm-61159
institution Universiti Sains Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T19:09:50Z
publishDate 2019
recordtype eprints
repository_type Digital Repository
spelling usm-611592024-09-19T03:27:28Z http://eprints.usm.my/61159/ Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification Ang, Sau Loong QA276-280 Mathematical Analysis 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. 2019-03 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/61159/1/Bayesian%20Networks%20with%20greedy%20cut.pdf Ang, Sau Loong (2019) Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification. PhD thesis, Universiti Sains Malaysia.
spellingShingle QA276-280 Mathematical Analysis
Ang, Sau Loong
Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification
title Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification
title_full Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification
title_fullStr Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification
title_full_unstemmed Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification
title_short Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification
title_sort bayesian networks with greedy backward elimination in feature selection for data classification
topic QA276-280 Mathematical Analysis
url http://eprints.usm.my/61159/
http://eprints.usm.my/61159/1/Bayesian%20Networks%20with%20greedy%20cut.pdf