Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions
The superparent one-dependence estimators (SPODEs) is a popular family of semi-naive Bayesian network classifiers, and the averaged one-dependence estimators (AODE) provides efficient single pass learning with competitive classification accuracy. All the SPODEs in AODE are treated equally and have t...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9169616/ |
| _version_ | 1818431117813874688 |
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| author | Hua Lou Gaojie Wang Limin Wang Musa Mammadov |
| author_facet | Hua Lou Gaojie Wang Limin Wang Musa Mammadov |
| author_sort | Hua Lou |
| collection | DOAJ |
| description | The superparent one-dependence estimators (SPODEs) is a popular family of semi-naive Bayesian network classifiers, and the averaged one-dependence estimators (AODE) provides efficient single pass learning with competitive classification accuracy. All the SPODEs in AODE are treated equally and have the same weight. Researchers have proposed to apply information-theoretic metrics, such as mutual information or conditional log likelihood, for assigning discriminative weights. However, while dealing with different instances the independence assumptions for different SPODEs may hold to different extents. The quest for highly scalable learning algorithms is urgent to approximate the ground-truth attribute dependencies that are implicit in training data. In this study we set each instance as the target and investigate extensions to AODE by measuring the independence assumption of SPODEs and assigning weights. The proposed approach, called independence weighted AODE (IWAODE), is validated on 40 benchmark datasets from the UCI machine learning repository. Experimental results reveal that, the resulting weighted SPODEs delivers computationally efficient low-bias learning, proving to be a competitive alternative to state-of-the-art single and ensemble Bayesian network classifiers (such as tree-augmented naive Bayes, k-dependence Bayesian classifier, WAODE-MI and etc). |
| first_indexed | 2024-12-14T15:44:13Z |
| format | Article |
| id | doaj.art-286d41bb8ae24afab31dac643cccab0a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:44:13Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-286d41bb8ae24afab31dac643cccab0a2022-12-21T22:55:32ZengIEEEIEEE Access2169-35362020-01-01815046515047710.1109/ACCESS.2020.30169849169616Model Weighting for One-Dependence Estimators by Measuring the Independence AssumptionsHua Lou0Gaojie Wang1Limin Wang2https://orcid.org/0000-0001-7742-669XMusa Mammadov3Department of Software and Big Data, Changzhou College of Information Technology, Changzhou, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaSchool of Information Technology, Deakin University, Burwood, VIC, AustraliaThe superparent one-dependence estimators (SPODEs) is a popular family of semi-naive Bayesian network classifiers, and the averaged one-dependence estimators (AODE) provides efficient single pass learning with competitive classification accuracy. All the SPODEs in AODE are treated equally and have the same weight. Researchers have proposed to apply information-theoretic metrics, such as mutual information or conditional log likelihood, for assigning discriminative weights. However, while dealing with different instances the independence assumptions for different SPODEs may hold to different extents. The quest for highly scalable learning algorithms is urgent to approximate the ground-truth attribute dependencies that are implicit in training data. In this study we set each instance as the target and investigate extensions to AODE by measuring the independence assumption of SPODEs and assigning weights. The proposed approach, called independence weighted AODE (IWAODE), is validated on 40 benchmark datasets from the UCI machine learning repository. Experimental results reveal that, the resulting weighted SPODEs delivers computationally efficient low-bias learning, proving to be a competitive alternative to state-of-the-art single and ensemble Bayesian network classifiers (such as tree-augmented naive Bayes, k-dependence Bayesian classifier, WAODE-MI and etc).https://ieeexplore.ieee.org/document/9169616/Averaged one-dependence estimatorsdiscriminative weightsconditional independence |
| spellingShingle | Hua Lou Gaojie Wang Limin Wang Musa Mammadov Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions IEEE Access Averaged one-dependence estimators discriminative weights conditional independence |
| title | Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions |
| title_full | Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions |
| title_fullStr | Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions |
| title_full_unstemmed | Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions |
| title_short | Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions |
| title_sort | model weighting for one dependence estimators by measuring the independence assumptions |
| topic | Averaged one-dependence estimators discriminative weights conditional independence |
| url | https://ieeexplore.ieee.org/document/9169616/ |
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