Ensemble Learning Based on Evidential Reasoning Rule with a New Weight Calculation Method

Xu, Cong; Zhou, Zhijie; He, Wei; Zhu, Hailong; Gao, Yanzi

doi:10.1007/978-3-030-88601-1_15

Cited by 2 publications

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References 6 publications

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“…The weight of evidence is an important part of the ER rule, and the weighting method is the focus of this paper. Some representative weighting methods [ 28 ] are selected for definition in this section. The weights identified in this section will be used in Section 4.2 .…”

Section: Construction Of Weighting Methodsmentioning

confidence: 99%

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

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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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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Section: Construction Of Weighting Methodsmentioning

confidence: 99%

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

Zhang

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the process of repeated experiments, the diference in the weight of evidence will lead to a gap of the accuracy of the integration results of more than 3%. Te weights used literature [17][18][19][20][21][22][23][24][25][26][27] are specifed by experts who actually evaluate industrial process indicators.…”

Section: Introductionmentioning

confidence: 99%

A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule

Zhang

Zhou

et al. 2023

Journal of Electrical and Computer Engineering

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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.

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Ensemble Learning Based on Evidential Reasoning Rule with a New Weight Calculation Method

Cited by 2 publications

References 6 publications

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

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

A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule

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