ZSLF: A New Soft Likelihood Function Based on Z-Numbers and Its Application in Expert Decision System

2021

Sensors

Multisource information fusion has received much attention in the past few decades, especially for the smart Internet of Things (IoT). Because of the impacts of devices, the external environment, and communication problems, the collected information may be uncertain, imprecise, or even conflicting. How to handle such kinds of uncertainty is still an open issue. Complex evidence theory (CET) is effective at disposing of uncertainty problems in the multisource information fusion of the IoT. In CET, however, how to measure the distance among complex basis belief assignments (CBBAs) to manage conflict is still an open issue, which is a benefit for improving the performance in the fusion process of the IoT. In this paper, therefore, a complex Pignistic transformation function is first proposed to transform the complex mass function; then, a generalized betting commitment-based distance (BCD) is proposed to measure the difference among CBBAs in CET. The proposed BCD is a generalized model to offer more capacity for measuring the difference among CBBAs. Additionally, other properties of the BCD are analyzed, including the non-negativeness, nondegeneracy, symmetry, and triangle inequality. Besides, a basis algorithm and its weighted extension for multi-attribute decision-making are designed based on the newly defined BCD. Finally, these decision-making algorithms are applied to cope with the medical diagnosis problem under the smart IoT environment to reveal their effectiveness.

Section: Preliminariesmentioning

confidence: 99%

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

2021

Sensors

“…Many mathematical models and theories have been proposed to process information in an uncertain environment. For instance, the extended soft sets, 6 fuzzy sets theory, 7 – 13 Dempster–Shafer (D‐S) evidence theory, 14 – 22 complex mass function, 23 – 27 D ‐numbers, 28 – 30 Z ‐numbers, 31 – 33 and so on. They are also widely used in many real‐world applications that include but not limited to risk evaluation, 34 multi‐attribute decision making, 35 – 38 and pattern classification 39 – 43 …”

Section: Introductionmentioning

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.

“…How to express the knowledge involved in sources of uncertain information still remains an open issue [7,8]. Hence, researchers have attempted to model and measure uncertain information using extended soft sets [9], evidence theory [10], reasoning [11][12][13], belief structures [14,15], D numbers [16,17], Z numbers [18,19], and other hybrid methods [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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

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.