The generalization negation of probability distribution and its application in target recognition based on sensor fusion

Gao, Xiaozhuan; Deng, Yong

doi:10.1177/1550147719849381

Cited by 39 publications

(38 citation statements)

References 62 publications

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“…Probability is a widely used tool to model uncertainty. 79 For some MCDM problems, the satisfactions for suppliers to criteria are represented by probability distributions. Generally, we need to address the need to order the probability distributions on the unit interval with respect to which is larger.…”

Section: The Probabilistic Exceedance Methodsmentioning

confidence: 99%

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An interval‐valued exceedance method in MCDM with uncertain satisfactions

Liu

Xiao

2019

Int J Intell Syst

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Multicriteria decision‐making problems have been applied to many applications for its practicality. Nevertheless, when the evaluated satisfactions are more complex, such as interval‐valued distributions, how to reasonably obtain the aggregation results of alternatives is still an open issue. In this paper, an interval‐valued exceedance method is proposed to solve such a question based on the Golden Rule representative value and probabilistic exceedance method. Due to good performance of expressing uncertain information, the Golden Rule representative value method is used to order interval‐valued satisfactions after an effective normalization process. In addition, a quantifier‐based ordered weighted averaging operator is also introduced to consider the preferences of decision makers. A realistic application of supplier selection is shown to illustrate the practicality of the proposed method.

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Section: The Probabilistic Exceedance Methodsmentioning

confidence: 99%

“…Probability is a widely used tool to model uncertainty . For some MCDM problems, the satisfactions for suppliers to criteria are represented by probability distributions.…”

Section: Preliminariesmentioning

confidence: 99%

An interval‐valued exceedance method in MCDM with uncertain satisfactions

Liu

Xiao

2019

Int J Intell Syst

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“…The entropy is a useful tool for measuring the information of a complex network; hence, it has been widely used in many applications, e.g., evaluations of the vulnerability [35], presentation of the dimension [24,17], dilemma experiments [6], data fusion [28,8], entanglement measures [40], and evidence theory [10,12]. In addition, the structure of a complex network, such as the nodes and links, can be seen as probability sets.…”

Section: Introductionmentioning

confidence: 99%

Identification of influencers in complex networks by local information dimensionality

Wen

Deng

2020

Information Sciences

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100

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The identification of influential spreaders in complex networks is a popular topic in studies of network characteristics. Many centrality measures have been proposed to address this problem, but most have limitations. In this paper, a method for identifying influencers in complex networks via the local information dimensionality is proposed. The proposed method considers the local structural properties around the central node; therefore, the scale of locality only increases to half of the maximum value of the shortest distance from the central node. Thus, the proposed method considers the quasilocal information and reduces the computational complexity. The information (number of nodes) in boxes is described via the Shannon entropy, which is more reasonable. A node is more influential when its local information dimensionality is higher. In order to show the effectiveness of the proposed method, five existing centrality measures are used as comparison methods to rank influential nodes in six real-world complex networks. In addition, a susceptible-infected (SI) model and Kendall's tau coefficient are applied to show the correlation between different methods. Experiment results show the superiority of the proposed method.

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“…The probability distribution function is a useful tool to model the uncertain information, and it has been applied in plenty fields including information fusion, target recognition, and so forth. The probability (belief) distribution function based on the power set also obtained lots of focuses, such as belief conflict, evolutionary games, supplier selection, and failure mode and effects analysis, prediction, and decision making …”

Section: Introductionmentioning

confidence: 99%

Extension of Yager's negation of a probability distribution based on Tsallis entropy

et al. 2019

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The negation of probability distribution becomes an important topic since some problems are burdensome to deal with directly. Inspired by Yager's negation of probability distribution, an extension model to measure the negation of a probability distribution is proposed using the idea of a nonextensive statistic based on Tsallis entropy. Proofs show that the proposed extension of negation of probability distribution converges to the maximum Tsallis entropy. The proposed model may extend Yager's method to consider the influences of the correlations in a system, which gives the different convergent routes. Some numerical simulation results are used to illustrate the effectiveness of the proposed methodology.

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The generalization negation of probability distribution and its application in target recognition based on sensor fusion

Cited by 39 publications

References 62 publications

An interval‐valued exceedance method in MCDM with uncertain satisfactions

An interval‐valued exceedance method in MCDM with uncertain satisfactions

Identification of influencers in complex networks by local information dimensionality

Extension of Yager's negation of a probability distribution based on Tsallis entropy

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