Probability Transform Based on the Ordered Weighted Averaging and Entropy Difference

Pan, Lipeng; Deng, Yong

doi:10.15837/ijccc.2020.4.3743

Cited by 52 publications

(28 citation statements)

References 73 publications

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“…56 Perhaps some decision methods 66,67 on these complicated fuzzy sets can be applied to security games of such fuzzy payoffs. (iv) The performance of some new uncertainty measures in information science (e.g., the pseudo-Pascal triangle based on the maximum Deng entropy, 68 probability transform based on the OWA and entropy difference, 69 the interval-valued Pythagorean-prioritised operator, 70 etc.) can be examined based on our framework.…”

Section: Discussionmentioning

confidence: 99%

A decision support framework for security resource allocation under ambiguity

Liu

McAreavey

et al. 2020

Int J Intell Syst

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There has been increasing interest in using Stackelberg game (known as a security game) to allocate limited security resources against different attacker types with a specific probability distribution. However, real problems of this kind often face ambiguous information, such as imprecise, unreliable and absent payoffs, and ambiguous assignments of these payoffs. To this end, based on decision theory and the Dempster-Shafer theory of evidence, this paper proposes a novel framework that can handle these common types of ambiguity. More specifically, this paper deploys the underlying principles of existing rules from decision theory, as a way to characterise different attitudes to ambiguity, during the transformation of ambiguous payoffs into point-valued payoffs. Hence, our framework holds some good properties: (i) it subsumes traditional security games without ambiguous payoffs, (ii) a uniform margin of error will not affect the results and (iii) the influence

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Section: Discussionmentioning

confidence: 99%

A decision support framework for security resource allocation under ambiguity

Liu

McAreavey

et al. 2020

Int J Intell Syst

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“…Deng entropy has attracted more and more attention [57][58][59][60][61] and has been improved from multiple perspectives [62][63][64][65][66] and applied in different fields [67][68][69][70][71]. For more details on Deng entropy, please refer to [25].…”

Section: The Definition Of Deng Entropymentioning

confidence: 99%

Uncertainty measure in evidence theory

Deng

2020

Sci. China Inf. Sci.

Self Cite

300

108

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As an extension of probability theory, evidence theory is able to better handle unknown and imprecise information. Owing to its advantages, evidence theory has more flexibility and effectiveness for modeling and processing uncertain information. Uncertainty measure plays an essential role both in evidence theory and probability theory. In probability theory, Shannon entropy provides a novel perspective for measuring uncertainty. Various entropies exist for measuring the uncertainty of basic probability assignment (BPA) in evidence theory. However, from the standpoint of the requirements of uncertainty measurement and physics, these entropies are controversial. Therefore, the process for measuring BPA uncertainty currently remains an open issue in the literature. Firstly, this paper reviews the measures of uncertainty in evidence theory followed by an analysis of some related controversies. Secondly, we discuss the development of Deng entropy as an effective way to measure uncertainty, including introducing its definition, analyzing its properties, and comparing it to other measures. We also examine the concept of maximum Deng entropy, the pseudo-Pascal triangle of maximum Deng entropy, generalized belief entropy, and measures of divergence. In addition, we conduct an analysis of the application of Deng entropy and further examine the challenges for future studies on uncertainty measurement in evidence theory. Finally, a conclusion is provided to summarize this study.

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“…It is well known that various methods have been presented 44‐48 . Among these methods, D‐S theory is one of the most effective method to handle imprecise and uncertainty, 49,50 which is used in many fields, such as target recognition, 51,52 evidential reasoning, 53‐55 classification, 56,57 industrial alarm systems, 58 decision making, 59‐62 and so on 63‐65 . The brief introduction to D‐S theory will be given as follows.…”

Section: Preliminariesmentioning

confidence: 99%

A method for combining conflicting evidences with improved distance function and Tsallis entropy

Xiao

2020

Int J Intell Syst

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For the sake of great ability of handling uncertain information, Dempster‐Shafer evidence theory is extensively used in information fusion. Nevertheless, when there exists highly inconsistent evidences, using classical Dempster's combination rule may lead to counter‐intuitive results. To address this issue, a new conflicting evidences combination method based on distance function and Tsallis entropy is proposed. Numerical examples are used to illustrate the feasibility and efficiency of the proposed method. Further, an fault diagnosis problem is used as an example to show the effectiveness and superiority of the proposed method. The proposed method outperforms other methods that the proposed method recognize the target by the probability 99.49%, which is higher than other methods.

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Probability Transform Based on the Ordered Weighted Averaging and Entropy Difference

Cited by 52 publications

References 73 publications

A decision support framework for security resource allocation under ambiguity

A decision support framework for security resource allocation under ambiguity

Uncertainty measure in evidence theory

A method for combining conflicting evidences with improved distance function and Tsallis entropy

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