Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators
Of numerous proposals for weakening the attribute independence assumption of Naive Bayes, averaged one-dependence estimators (AODE) learns by extrapolation from marginal to full-multivariate probability distributions, and has demonstrated reasonable improvement in terms of classification performance...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8984347/ |
| _version_ | 1818875656224636928 |
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| author | Limin Wang Jie Chen Yang Liu Minghui Sun |
| author_facet | Limin Wang Jie Chen Yang Liu Minghui Sun |
| author_sort | Limin Wang |
| collection | DOAJ |
| description | Of numerous proposals for weakening the attribute independence assumption of Naive Bayes, averaged one-dependence estimators (AODE) learns by extrapolation from marginal to full-multivariate probability distributions, and has demonstrated reasonable improvement in terms of classification performance. However, all the one-dependence estimators in AODE are assigned with the same weight, and their probability estimates are combined linearly. This work presents an efficient and effective attribute value weighting approach that assigns discriminative weights to different super-parent one-dependence estimators for different instances by identifying the differences among these one-dependence estimators in terms of log likelihood. The proposed approach is validated on widely used benchmark datasets from UCI machine learning repository. Experimental results show that the proposed approach achieves bias-variance trade-off and is a competitive alternative to state-of-the-art Bayesian and non-Bayesian learners (e.g., tree augmented Naive Bayes and logistic regression). |
| first_indexed | 2024-12-19T13:29:58Z |
| format | Article |
| id | doaj.art-22815603082d4238b50cca9f91cd3e94 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:29:58Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-22815603082d4238b50cca9f91cd3e942022-12-21T20:19:26ZengIEEEIEEE Access2169-35362020-01-018278872790010.1109/ACCESS.2020.29717068984347Self-Adaptive Attribute Value Weighting for Averaged One-Dependence EstimatorsLimin Wang0https://orcid.org/0000-0001-7742-669XJie Chen1https://orcid.org/0000-0003-0988-153XYang Liu2https://orcid.org/0000-0003-4136-361XMinghui Sun3https://orcid.org/0000-0002-1809-8187College of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaOf numerous proposals for weakening the attribute independence assumption of Naive Bayes, averaged one-dependence estimators (AODE) learns by extrapolation from marginal to full-multivariate probability distributions, and has demonstrated reasonable improvement in terms of classification performance. However, all the one-dependence estimators in AODE are assigned with the same weight, and their probability estimates are combined linearly. This work presents an efficient and effective attribute value weighting approach that assigns discriminative weights to different super-parent one-dependence estimators for different instances by identifying the differences among these one-dependence estimators in terms of log likelihood. The proposed approach is validated on widely used benchmark datasets from UCI machine learning repository. Experimental results show that the proposed approach achieves bias-variance trade-off and is a competitive alternative to state-of-the-art Bayesian and non-Bayesian learners (e.g., tree augmented Naive Bayes and logistic regression).https://ieeexplore.ieee.org/document/8984347/Attribute value weightingaveraged one-dependence estimatorslog likelihoodentropy |
| spellingShingle | Limin Wang Jie Chen Yang Liu Minghui Sun Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators IEEE Access Attribute value weighting averaged one-dependence estimators log likelihood entropy |
| title | Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators |
| title_full | Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators |
| title_fullStr | Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators |
| title_full_unstemmed | Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators |
| title_short | Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators |
| title_sort | self adaptive attribute value weighting for averaged one dependence estimators |
| topic | Attribute value weighting averaged one-dependence estimators log likelihood entropy |
| url | https://ieeexplore.ieee.org/document/8984347/ |
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