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Exact Learning Augmented Naive Bayes Classifier
Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700436/ https://www.ncbi.nlm.nih.gov/pubmed/34946009 http://dx.doi.org/10.3390/e23121703 |
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author | Sugahara, Shouta Ueno, Maomi |
author_facet | Sugahara, Shouta Ueno, Maomi |
author_sort | Sugahara, Shouta |
collection | PubMed |
description | Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between the performances of the two scores in the earlier studies may be attributed to the fact that they used approximate learning algorithms, not exact ones. This paper compares the classification accuracies of BNs with approximate learning using CLL to those with exact learning using ML. The results demonstrate that the classification accuracies of BNs obtained by maximizing the ML are higher than those obtained by maximizing the CLL for large data. However, the results also demonstrate that the classification accuracies of exact learning BNs using the ML are much worse than those of other methods when the sample size is small and the class variable has numerous parents. To resolve the problem, we propose an exact learning augmented naive Bayes classifier (ANB), which ensures a class variable with no parents. The proposed method is guaranteed to asymptotically estimate the identical class posterior to that of the exactly learned BN. Comparison experiments demonstrated the superior performance of the proposed method. |
format | Online Article Text |
id | pubmed-8700436 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87004362021-12-24 Exact Learning Augmented Naive Bayes Classifier Sugahara, Shouta Ueno, Maomi Entropy (Basel) Article Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between the performances of the two scores in the earlier studies may be attributed to the fact that they used approximate learning algorithms, not exact ones. This paper compares the classification accuracies of BNs with approximate learning using CLL to those with exact learning using ML. The results demonstrate that the classification accuracies of BNs obtained by maximizing the ML are higher than those obtained by maximizing the CLL for large data. However, the results also demonstrate that the classification accuracies of exact learning BNs using the ML are much worse than those of other methods when the sample size is small and the class variable has numerous parents. To resolve the problem, we propose an exact learning augmented naive Bayes classifier (ANB), which ensures a class variable with no parents. The proposed method is guaranteed to asymptotically estimate the identical class posterior to that of the exactly learned BN. Comparison experiments demonstrated the superior performance of the proposed method. MDPI 2021-12-20 /pmc/articles/PMC8700436/ /pubmed/34946009 http://dx.doi.org/10.3390/e23121703 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sugahara, Shouta Ueno, Maomi Exact Learning Augmented Naive Bayes Classifier |
title | Exact Learning Augmented Naive Bayes Classifier |
title_full | Exact Learning Augmented Naive Bayes Classifier |
title_fullStr | Exact Learning Augmented Naive Bayes Classifier |
title_full_unstemmed | Exact Learning Augmented Naive Bayes Classifier |
title_short | Exact Learning Augmented Naive Bayes Classifier |
title_sort | exact learning augmented naive bayes classifier |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700436/ https://www.ncbi.nlm.nih.gov/pubmed/34946009 http://dx.doi.org/10.3390/e23121703 |
work_keys_str_mv | AT sugaharashouta exactlearningaugmentednaivebayesclassifier AT uenomaomi exactlearningaugmentednaivebayesclassifier |