A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling
Due to the complexity of system and lack of expertise, epistemic uncertainties may present in the experts’ judgment on the importance of certain indices during group decision-making. A novel combination weighting method is proposed to solve the index weighting problem when various uncertainties are...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2017/8751683 |
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| _version_ | 1849400464563503104 |
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| author | Ying Yan Bin Suo |
| author_facet | Ying Yan Bin Suo |
| author_sort | Ying Yan |
| collection | DOAJ |
| description | Due to the complexity of system and lack of expertise, epistemic uncertainties may present in the experts’ judgment on the importance of certain indices during group decision-making. A novel combination weighting method is proposed to solve the index weighting problem when various uncertainties are present in expert comments. Based on the idea of evidence theory, various types of uncertain evaluation information are uniformly expressed through interval evidence structures. Similarity matrix between interval evidences is constructed, and expert’s information is fused. Comment grades are quantified using the interval number, and cumulative probability function for evaluating the importance of indices is constructed based on the fused information. Finally, index weights are obtained by Monte Carlo random sampling. The method can process expert’s information with varying degrees of uncertainties, which possesses good compatibility. Difficulty in effectively fusing high-conflict group decision-making information and large information loss after fusion is avertible. Original expert judgments are retained rather objectively throughout the processing procedure. Cumulative probability function constructing and random sampling processes do not require any human intervention or judgment. It can be implemented by computer programs easily, thus having an apparent advantage in evaluation practices of fairly huge index systems. |
| format | Article |
| id | doaj-art-89a8a374cbb8442a95a9042658d25a2c |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-89a8a374cbb8442a95a9042658d25a2c2025-08-20T03:38:02ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2017-01-01201710.1155/2017/87516838751683A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random SamplingYing Yan0Bin Suo1School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, ChinaInstitute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621900, ChinaDue to the complexity of system and lack of expertise, epistemic uncertainties may present in the experts’ judgment on the importance of certain indices during group decision-making. A novel combination weighting method is proposed to solve the index weighting problem when various uncertainties are present in expert comments. Based on the idea of evidence theory, various types of uncertain evaluation information are uniformly expressed through interval evidence structures. Similarity matrix between interval evidences is constructed, and expert’s information is fused. Comment grades are quantified using the interval number, and cumulative probability function for evaluating the importance of indices is constructed based on the fused information. Finally, index weights are obtained by Monte Carlo random sampling. The method can process expert’s information with varying degrees of uncertainties, which possesses good compatibility. Difficulty in effectively fusing high-conflict group decision-making information and large information loss after fusion is avertible. Original expert judgments are retained rather objectively throughout the processing procedure. Cumulative probability function constructing and random sampling processes do not require any human intervention or judgment. It can be implemented by computer programs easily, thus having an apparent advantage in evaluation practices of fairly huge index systems.http://dx.doi.org/10.1155/2017/8751683 |
| spellingShingle | Ying Yan Bin Suo A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling Discrete Dynamics in Nature and Society |
| title | A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling |
| title_full | A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling |
| title_fullStr | A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling |
| title_full_unstemmed | A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling |
| title_short | A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling |
| title_sort | combined weighting method based on hybrid of interval evidence fusion and random sampling |
| url | http://dx.doi.org/10.1155/2017/8751683 |
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