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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/12/1703 |
| _version_ | 1827672569214402560 |
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| author | Shouta Sugahara Maomi Ueno |
| author_facet | Shouta Sugahara Maomi Ueno |
| author_sort | Shouta Sugahara |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T04:11:08Z |
| format | Article |
| id | doaj.art-674b368858624390ba6b24c045e780d4 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T04:11:08Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-674b368858624390ba6b24c045e780d42023-11-23T08:11:55ZengMDPI AGEntropy1099-43002021-12-012312170310.3390/e23121703Exact Learning Augmented Naive Bayes ClassifierShouta Sugahara0Maomi Ueno1Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo 182-8585, JapanGraduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo 182-8585, JapanEarlier 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.https://www.mdpi.com/1099-4300/23/12/1703augmented naive Bayes classifierBayesian networksclassificationstructure learning |
| spellingShingle | Shouta Sugahara Maomi Ueno Exact Learning Augmented Naive Bayes Classifier Entropy augmented naive Bayes classifier Bayesian networks classification structure learning |
| 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 | augmented naive Bayes classifier Bayesian networks classification structure learning |
| url | https://www.mdpi.com/1099-4300/23/12/1703 |
| work_keys_str_mv | AT shoutasugahara exactlearningaugmentednaivebayesclassifier AT maomiueno exactlearningaugmentednaivebayesclassifier |