A Deng-Entropy-Based Evidential Reasoning Approach for Multi-expert Multi-criterion Decision-Making with Uncertainty

DOI

10.2991/ijcis.d.200814.001How to use a DOI?

Keywords

Multi-expert multi-criterion decision-making; Evidential reasoning; Deng entropy; Uncertainty; Lung cancer

Abstract

The evidential reasoning (ER) approach has been widely applied to aggregate evaluation information in multi-expert multi-criterion decision-making (MEMCDM) problems with uncertainties. However, the comprehensive results derived by the ER approach remain uncertain. In this study, we propose a Deng-entropy-based ER approach for MEMCDM problems to reduce the uncertainty. Firstly, we reassign the remaining belief of the uncertain evaluation information to the focal elements of the given evaluations. Afterward, we introduce the Deng entropy to respectively calculate the objective weights of criteria and those of experts, so as to reduce the subjective uncertainty in MEMCDM. Then, the ER approach is applied twice to generate the comprehensive evaluations of alternatives. A method is introduced to rank alternatives corresponding to their comprehensive evaluations, forming a Deng-entropy-based ER approach for MEMCDM problems with uncertainty. An illustrative example of screening the people at high risk of lung cancer is provided, and comparative analyses are given to show the rationality and superiority of the proposed method.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Huchang Liao
AU  - Zhongyuan Ren
AU  - Ran Fang
PY  - 2020
DA  - 2020/08/26
TI  - A Deng-Entropy-Based Evidential Reasoning Approach for Multi-expert Multi-criterion Decision-Making with Uncertainty
JO  - International Journal of Computational Intelligence Systems
SP  - 1281
EP  - 1294
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200814.001
DO  - 10.2991/ijcis.d.200814.001
ID  - Liao2020
ER  -