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

Xu, Cong; Zhang, YunYi; Zhang, Wei; Zu, Hongquan; Zhang, YiZhe; He, Wei

doi:10.1155/2022/1156748

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…The combined weight (Eq. 11) is obtained by using the multiplication synthesis normalization method (Xu et al, 2022) to integrate the subjective weight (obtained by Delphi method) and objective weight (obtained by random forest feature recognition method) calculations mentioned above. The formula for the multiplication synthesis normalization method is:…”

Section: Weight Determination Based On Comprehensive Weighting Methodsmentioning

confidence: 99%

A coal bursting liability evaluation model based on fuzzy set theory and analysis of three influencing factors

Wang,

Jin,

Liu

et al. 2024

Front. Earth Sci.

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The classification of coal bursting liability is of great significance for the prevention and control of rock burst. To address the shortcomings in existing bursting liability classification methods, a comprehensive evaluation model for bursting liability based on a combination of weighted-fuzzy set theory and three influencing factor analyses is proposed. The model selects four evaluation indicators: dynamic failure time (DT), elastic energy index (WET), bursting energy index (KE), and uniaxial compressive strength (RC). Two types of membership functions, trapezoidal fuzzy numbers (TFN) and Gaussian fuzzy numbers (GFN), are used to quantitatively describe the fuzziness between indicator levels. The Delphi method and a random forest feature identification method are combined to obtain a subjective and objective combined weighting, determining the optimal combination weight of the four indicators. Based on Zadeh operator (ZO), maximum-minimum operator (MMO), weighted-average operator (WAO), and all-around restrictive operator (ARO), calculations are carried out for the synthesis of indicator weights and memberships. Maximal membership principle (MMP) and Credible identification principle (CIP) are utilized as evaluation principle to assess the bursting liability level, constructing 16 fuzzy comprehensive evaluation models. The impact of membership functions, fuzzy operators, and evaluation principle on evaluation results are systematically analyzed based on the discrimination results of 127 sample sets. The results show that the optimal fuzzy comprehensive evaluation model is constructed using the trapezoidal fuzzy numbers, weighted average operator, and maximal membership principle (TFN-WAO-MMP), with a classification accuracy of 97.64%. Finally, the optimal model is applied to 10 engineering instances, and the evaluation results are consistent with the actual situation, verifying the reliability and effectiveness of the model. Overall, these findings contribute to the development of a more sophisticated and accurate method for assessing the rock burst tendency of coal specimens. By leveraging the theory of fuzzy sets, this approach provides a more nuanced and nuanced evaluation of rock burst tendency, and thus offers the potential to improve workplace safety and efficiency in the coal mining industry.

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Section: Weight Determination Based On Comprehensive Weighting Methodsmentioning

confidence: 99%

A coal bursting liability evaluation model based on fuzzy set theory and analysis of three influencing factors

Wang,

Jin,

Liu

et al. 2024

Front. Earth Sci.

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“…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%

“…Previous studies [23,24] have proposed the validity and reliability of combined weighting. Literature [25]the combinatorial weighting method is adopted to improve the accuracy of ensemble learning method based on evidential reasoning rules, but the stability of combinatorial weighting method is not explained, and the weight is not disturbed fusion. It can be said that solving the stability and reliability of evidence weight is the primary task of ensemble learning methods based on evidential reasoning rules.…”

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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Section: Weight Determination Based On Comprehensive Weighting Methodsmentioning

confidence: 99%

Table1.DOCX

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

Cited by 4 publications

References 22 publications

A coal bursting liability evaluation model based on fuzzy set theory and analysis of three influencing factors

A coal bursting liability evaluation model based on fuzzy set theory and analysis of three influencing factors

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

Table1.DOCX

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