Energy and entropy: Path from game theory to statistical mechanics

Journal of Healthcare Engineering

Yue

2021

In decision-making systems, how to measure uncertain information remains an open issue, especially for information processing modeled on complex planes. In this paper, a new complex entropy is proposed to measure the uncertainty of a complex-valued distribution (CvD). The proposed complex entropy is a generalization of Gini entropy that has a powerful capability to measure uncertainty. In particular, when a CvD reduces to a probability distribution, the complex entropy will degrade into Gini entropy. In addition, the properties of complex entropy, including the nonnegativity, maximum and minimum entropies, and boundedness, are analyzed and discussed. Several numerical examples illuminate the superiority of the newly defined complex entropy. Based on the newly defined complex entropy, a multisource information fusion algorithm for decision-making is developed. Finally, we apply the decision-making algorithm in a medical diagnosis problem to validate its practicability.

Section: Entropy For Cvdmentioning

confidence: 99%

Complex Entropy and Its Application in Decision-Making for Medical Diagnosis

Journal of Healthcare Engineering

Yue

2021

“…D‐S evidence theory, which is the generalized form of classic Bayesian theory of probability, satisfies a weaker condition than classic Bayesian theory, which has attracted many researchers' attention for its great ability to handle uncertain information. D‐S evidence theory is extensively used in massive applications in the real world 44 – 49 . However, in the framework of D‐S evidence theory, the classic Dempster's combination rule fails to fuse information correctly when facing highly conflicting evidence 50 .…”

Section: Introductionmentioning

confidence: 99%

“…D-S evidence theory is extensively used in massive applications in the real world. [44][45][46][47][48][49] However, in the framework of D-S evidence theory, the classic Dempster's combination rule fails to fuse information correctly when facing highly conflicting evidence. 50 Hence, there are many studies that focus on how to fuse conflicting evidence correctly.…”

mentioning

confidence: 99%

Conflicting management of evidence combination from the point of improvement of basic probability assignment

2021

Int J Intell Syst

An open issue of the Dempster combination rule is the conflicting management, which is very important in multisource data fusion, such as group decision making and target recognition. To address this issue, an improved method to generate basic probability assignment is presented. Then, a new combination method to assign the conflicting mass function without the normalization is proposed to handle a highly conflicting environment. Compared with other methods, this proposed method is convenient in computing and has better accuracy to predict potential possibilities especially when disposing of extreme status. Some numerical examples and real benchmark data collected in UCI database are illustrated to verify the validity and rationality of the proposed method.

“…In real-numbers world, uncertainty [1][2][3] is an inevitable problem in decision making [4,5], especially in the field of information fusion [6]. Plenty of methodologies had been developed, such as D-number [7,8], Z-number [9,10], ordered weighted averaging method [11], entropy [12][13][14], Dengentropy-based [15] and classical Dempster-Shafer's evidence theory (D-S evidence theory) [16][17][18][19]. Those relevant theories have been widely used in uncertainty [20], such as fuzzy set processing [21][22][23][24], soft set processing [25][26][27], fault diagnosis [28,29], data fusion [30][31][32], group design [33,34],…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Complex Evidence Combination with Its Application in Multisource Information Fusion

2021

Preprint

With the development of evidence theory, classical Dempster-Shafer evidence theory has been extended to complex plane, called complex evidence theory. However, counterintuitive result may occurs in the case when fusing conflicting complex evidences. To address this problem, a new multisource information fusion method is proposed by means of complex evidential distance function. This proposed method can reduce the impact of abnormal complex evidence on the fusion results to better support decision. A numerical example and an application of medical diagnosis verify the feasibility and effectiveness of the proposed fusion method.