Cluster-based information fusion for probabilistic risk analysis in complex projects under uncertainty

Interval type-2 fuzzy sets (IT2 FS) play an important part in dealing with uncertain applications. However, how to measure the uncertainty of IT2 FS is still an open issue. The specific objective of this study is to present a new entropy named fuzzy belief entropy to solve the problem based on the relation among IT2 FS, belief structure, and Z-valuations. The interval of membership function can be transformed to interval BPA [Bel,Pl]. Then, Bel and Pl are put into the proposed entropy to calculate the uncertainty from the three aspects of fuzziness, discord, and nonspecificity, respectively, which makes the result more reasonable. Compared with other methods, fuzzy belief entropy is more reasonable because it can measure the uncertainty caused by multielement fuzzy subsets. Furthermore, when the membership function belongs to type-1 fuzzy sets, fuzzy belief entropy degenerates to Shannon entropy. Compared with other methods, several numerical examples are demonstrated that the proposed entropy is feasible and persuasive.

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“…As a classical data fusion theory, D-S evidence theory is widely applied in the field of uncertainty [54,55]. Here are some essential concepts:…”

Section: Basic Concepts In Belief Structurementioning

confidence: 99%

Uncertainty of Interval Type-2 Fuzzy Sets Based on Fuzzy Belief Entropy

Liu

Cai

2021

Entropy

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“…To solve these problems, a data-driven risk assessment model, based on Dempster-Shafer evidence theory (DST) [20,21], Deng entropy [22] and risk matrix [23], is proposed. Due to effectively deal with uncertain information, DST is widely used in decisionmaking [24][25][26], risk analysis [27], information fusion [28,29], uncertainty measurements [30], fault diagnosis [31][32][33], time-series [34], IoT applications [35] and many other fields [36,37]. Since most experts prefer to express their opinions with linguistic information, such as good, better, best, bad, worse, worst, DST can effectively deal with uncertain information about linguistic expressions involved in risk evaluation [38,39].…”

Section: Introductionmentioning

confidence: 99%

An Evidential Software Risk Evaluation Model

Deng

2022

Mathematics

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Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.

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Multi-criteria group decision-making methods with dynamic probabilistic linguistic information characterized by multiple consecutive time points

Dong,

Cheng,

et al. 2023

Int. J. Mach. Learn. & Cyber.

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Cluster-based information fusion for probabilistic risk analysis in complex projects under uncertainty

Cited by 9 publications

References 56 publications

Uncertainty of Interval Type-2 Fuzzy Sets Based on Fuzzy Belief Entropy

Uncertainty of Interval Type-2 Fuzzy Sets Based on Fuzzy Belief Entropy

An Evidential Software Risk Evaluation Model

Multi-criteria group decision-making methods with dynamic probabilistic linguistic information characterized by multiple consecutive time points

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