A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule
Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/8987461 |
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| author | YiZhe Zhang YunYi Zhang GuoHui Zhou Wei Zhang KangLe Li QuanQi Mu Wei He Kai Tang |
| author_facet | YiZhe Zhang YunYi Zhang GuoHui Zhou Wei Zhang KangLe Li QuanQi Mu Wei He Kai Tang |
| author_sort | YiZhe Zhang |
| collection | DOAJ |
| description | Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining effective information from the classifiers and cannot effectively reflect the relationship between different classifiers. Ensemble learning based on the evidential inference rule (ER rule) can effectively excavate the internal relationships among different classifiers and has a certain interpretability. However, the ER rule depends on the weight distribution of different combination strategies, and the setting of the evidence weight will affect the accuracy and stability of the model. Therefore, this paper proposes a new ensemble learning method based on multiple fusion weighted evidential reasoning rules and constructs an ensemble learning framework for data fusion and decision mapping. This framework takes the evidence weight, confidence, and feature data of each classifier as input and the integration results as output. The weight of evidence was determined by multiple fusion weights of the entropy weight method and order relation method. Finally, the integrated learning process is set up by the ER algorithm. The method proposed in this paper is verified by multiple datasets. Experimental results show that the surface construction model has good performance, and the defects of single weighting instability are greatly improved under the premise of improving the integration effect. |
| format | Article |
| id | doaj-art-1cba521a1b0a40dc814c64fbdf1d4e95 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-1cba521a1b0a40dc814c64fbdf1d4e952025-08-20T03:54:51ZengWileyJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/8987461A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning RuleYiZhe Zhang0YunYi Zhang1GuoHui Zhou2Wei Zhang3KangLe Li4QuanQi Mu5Wei He6Kai Tang7Harbin Normal UniversityHarbin Normal UniversityHarbin Normal UniversityHarbin Normal UniversityHarbin Finance UniversityHarbin Normal UniversityHarbin Normal UniversityHarbin Normal UniversityEnsemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining effective information from the classifiers and cannot effectively reflect the relationship between different classifiers. Ensemble learning based on the evidential inference rule (ER rule) can effectively excavate the internal relationships among different classifiers and has a certain interpretability. However, the ER rule depends on the weight distribution of different combination strategies, and the setting of the evidence weight will affect the accuracy and stability of the model. Therefore, this paper proposes a new ensemble learning method based on multiple fusion weighted evidential reasoning rules and constructs an ensemble learning framework for data fusion and decision mapping. This framework takes the evidence weight, confidence, and feature data of each classifier as input and the integration results as output. The weight of evidence was determined by multiple fusion weights of the entropy weight method and order relation method. Finally, the integrated learning process is set up by the ER algorithm. The method proposed in this paper is verified by multiple datasets. Experimental results show that the surface construction model has good performance, and the defects of single weighting instability are greatly improved under the premise of improving the integration effect.http://dx.doi.org/10.1155/2023/8987461 |
| spellingShingle | YiZhe Zhang YunYi Zhang GuoHui Zhou Wei Zhang KangLe Li QuanQi Mu Wei He Kai Tang A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule Journal of Electrical and Computer Engineering |
| title | A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule |
| title_full | A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule |
| title_fullStr | A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule |
| title_full_unstemmed | A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule |
| title_short | A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule |
| title_sort | new ensemble learning method for multiple fusion weighted evidential reasoning rule |
| url | http://dx.doi.org/10.1155/2023/8987461 |
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