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. 2022 Mar 7:2022:1156748.
doi: 10.1155/2022/1156748. eCollection 2022.

An Ensemble Learning Method Based on an Evidential Reasoning Rule considering Combination Weighting

Cong Xu  1 , YunYi Zhang  1 , Wei Zhang  1 , HongQuan Zu  2 , YiZhe Zhang  1 , Wei He  1   3
Affiliations

An Ensemble Learning Method Based on an Evidential Reasoning Rule considering Combination Weighting

Cong Xu et al. Comput Intell Neurosci. .

Abstract

As an extension of Dempster-Shafer (D-S) theory, the evidential reasoning (ER) rule can be used as a combination strategy in ensemble learning to deeply mine classifier information through decision-making reasoning. The weight of evidence is an important parameter in the ER rule, which has a significant effect on the result of ensemble learning. However, current research results on the weight of evidence are not ideal, leveraging expert knowledge to assign weights leads to the excessive subjectivity, and using sample statistical methods to assign weights relies too heavily on the samples, so the determined weights sometimes differ greatly from the actual importance of the attributes. Therefore, to solve the problem of excessive subjectivity and objectivity of the weights of evidence, and further improve the accuracy of ensemble learning based on the ER rule, we propose a novel combination weighting method to determine the weight of evidence. The combined weights are calculated by leveraging our proposed method to combine subjective and objective weights of evidence. The regularization of these weights is studied. Then, the evidential reasoning rule is used to integrate different classifiers. Five case studies of image classification datasets have been conducted to demonstrate the effectiveness of the combination weighting method.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Ensemble learning model based on the ER rule considering combination weighting.
Figure 2
Figure 2
The process of weights combination.
Figure 3
Figure 3
The process of ensemble learning based on the ER rule.
Figure 4
Figure 4
The process of weight of evidence assignment.
Figure 5
Figure 5
Comparison of ensemble accuracy of the big fish dataset.
Figure 6
Figure 6
Comparison of ensemble accuracy of the weather dataset.
Figure 7
Figure 7
Comparison of ensemble accuracy of the flower dataset.
Figure 8
Figure 8
Comparison of ensemble accuracy of the fruit 360 dataset.
Figure 9
Figure 9
Comparison of significance levels of five datasets.
See this image and copyright information in PMC

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