scholarly journals An Improved Belief Entropy and Its Application in Decision-Making

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Deyun Zhou ◽  
Yongchuan Tang ◽  
Wen Jiang
Keyword(s):  
Decision Making ◽  
Mass Function ◽  
The Body ◽  
Uncertain Information ◽  
Uncertainty Measure ◽  
Numerical Examples ◽  
Fusion Applications ◽  
Belief Entropy ◽  
The Difference ◽  
Available Information

Uncertainty measure in data fusion applications is a hot topic; quite a few methods have been proposed to measure the degree of uncertainty in Dempster-Shafer framework. However, the existing methods pay little attention to the scale of the frame of discernment (FOD), which means a loss of information. Due to this reason, the existing methods cannot measure the difference of uncertain degree among different FODs. In this paper, an improved belief entropy is proposed in Dempster-Shafer framework. The proposed belief entropy takes into consideration more available information in the body of evidence (BOE), including the uncertain information modeled by the mass function, the cardinality of the proposition, and the scale of the FOD. The improved belief entropy is a new method for uncertainty measure in Dempster-Shafer framework. Based on the new belief entropy, a decision-making approach is designed. The validity of the new belief entropy is verified according to some numerical examples and the proposed decision-making approach.

scholarly journals An Improved Total Uncertainty Measure in the Evidence Theory and Its Application in Decision Making

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 487 ◽  
Author(s):  
Miao Qin ◽  
Yongchuan Tang ◽  
Junhao Wen
Keyword(s):  
Decision Making ◽  
Evidence Theory ◽  
Uncertain Information ◽  
Uncertainty Measure ◽  
Total Uncertainty ◽  
Practical Applications ◽  
Basic Probability ◽  
Belief Entropy ◽  
Element Number ◽  
Theory Framework

Dempster–Shafer evidence theory (DS theory) has some superiorities in uncertain information processing for a large variety of applications. However, the problem of how to quantify the uncertainty of basic probability assignment (BPA) in DS theory framework remain unresolved. The goal of this paper is to define a new belief entropy for measuring uncertainty of BPA with desirable properties. The new entropy can be helpful for uncertainty management in practical applications such as decision making. The proposed uncertainty measure has two components. The first component is an improved version of Dubois–Prade entropy, which aims to capture the non-specificity portion of uncertainty with a consideration of the element number in frame of discernment (FOD). The second component is adopted from Nguyen entropy, which captures conflict in BPA. We prove that the proposed entropy satisfies some desired properties proposed in the literature. In addition, the proposed entropy can be reduced to Shannon entropy if the BPA is a probability distribution. Numerical examples are presented to show the efficiency and superiority of the proposed measure as well as an application in decision making.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fuyuan Xiao ◽  
Xiao-Guang Yue
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Medical Diagnosis ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Numerical Examples ◽  
Open Issue ◽  
Complex Planes ◽  
Complex Valued ◽  
Complex Entropy

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.

scholarly journals An Uncertainty Measure for Interval-valued Evidences

2017 ◽  
Vol 12 (5) ◽  
pp. 631 ◽  
Author(s):  
Wen Jiang ◽  
Shiyu Wang
Keyword(s):  
Evidence Theory ◽  
Uncertain Information ◽  
Uncertainty Measure ◽  
Interval Numbers ◽  
Numerical Examples ◽  
Belief Structure ◽  
Belief Structures ◽  
Open Issue ◽  
Deng Entropy ◽  
Interval Valued

Interval-valued belief structure (IBS), as an extension of single-valued belief structures in Dempster-Shafer evidence theory, is gradually applied in many fields. An IBS assigns belief degrees to interval numbers rather than precise numbers, thereby it can handle more complex uncertain information. However, how to measure the uncertainty of an IBS is still an open issue. In this paper, a new method based on Deng entropy denoted as UIV is proposed to measure the uncertainty of the IBS. Moreover, it is proved that UIV meets some desirable axiomatic requirements. Numerical examples are shown in the paper to demonstrate the efficiency of UIV by comparing the proposed UIV with existing approaches. 

scholarly journals An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Chen ◽  
Yongchuan Tang ◽  
Yan Lei
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Mass Function ◽  
Sensor Data ◽  
Artificial Data ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Fusion Applications ◽  
Basic Probability ◽  
Belief Entropy

Uncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting sensor data fusion since counterintuitive results may be attained. In order to handle this issue, a new method for data fusion based on weighted belief entropy and the negation of basic probability assignment (BPA) is proposed. First, the negation of BPA is applied to represent the information in a novel view. Then, by measuring the uncertainty of the evidence, the weighted belief entropy is adopted to indicate the relative importance of evidence. Finally, the ultimate weight of each body of evidence is applied to adjust the mass function before fusing by the Dempster combination rule. The validity of the proposed method is demonstrated in accordance with an experiment on artificial data and an application on fault diagnosis.

scholarly journals An improved belief entropy–based uncertainty management approach for sensor data fusion

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771849 ◽  
Author(s):  
Yongchuan Tang ◽  
Deyun Zhou ◽  
Zichang He ◽  
Shuai Xu
Keyword(s):  
Data Fusion ◽  
Uncertainty Management ◽  
The Body ◽  
Sensor Data ◽  
Management Approach ◽  
Uncertainty Measure ◽  
Sensor Data Fusion ◽  
Belief Entropy ◽  
Objective Uncertainty

In real applications, sensors may work in complicated environments; thus, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. To address this issue, an improved belief entropy–based uncertainty management approach for sensor data fusion is proposed in this article. First, the sensor report is modeled as the body of evidence in Dempster–Shafer framework. Then, the uncertainty measure of each body of evidence is based on the subjective uncertainty represented as the evidence sufficiency and evidence importance, and the objective uncertainty measure is expressed as the improved belief entropy. Evidence modification of conflict sensor data is based on the proposed uncertainty management approach before evidence fusion with Dempster’s rule of combination. Finally, the fusion result can be applied in real applications. A case study on sensor data fusion for fault diagnosis is presented to show the rationality of the proposed method.

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

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 840
Author(s):  
Fuyuan Xiao
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Medical Diagnosis ◽  
Evidence Theory ◽  
Mass Function ◽  
Communication Problems ◽  
Multi Attribute Decision Making ◽  
Open Issue ◽  
The Difference ◽  
Complex Basis

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.

TWO GENERALIZATIONS OF AGGREGATED UNCERTAINTY MEASURE FOR EVALUATION OF DEZERT–SMARANDACHE THEORY

2012 ◽  
Vol 11 (01) ◽  
pp. 119-142 ◽  
Author(s):  
MAHDI KHODABANDEH ◽  
ALIREZA MOHAMMAD-SHAHRI
Keyword(s):  
Decision Making ◽  
Belief Function ◽  
Localization Problem ◽  
Uncertainty Measure ◽  
Total Uncertainty ◽  
Dempster Shafer Theory ◽  
Uncertainty Measures ◽  
The Difference ◽  
Shafer Theory

Generality of the model which is used in Dezert–Smarandache Theory (DSmT) rather than other fusion algorithms such as Dempster–Shafer theory and capability of DSmT for dealing with highly conflict problems are two main reasons to prefer DSmT for decision-making systems. Aggregated uncertainty measure, which is called AU measure, has been introduced for Dempster–Shafer theory as one of the best presented ways to quantify the total uncertainty or the ambiguity of a belief function. Since AU cannot be applied to DSmT, two generalized aggregated uncertainty measures for DSmT, which are called GAU1 measure and GAU2 measure, are proposed in this paper. The GAU1 measure is developed by extension of the frame of discernment with distinct sub-events. The GAU2 measure is developed by considering new conditions on probability assignments which are used in the uncertainty measure. The new conditions are the difference of this measure with AU measure. A rigorous discussion is presented to validate that the proposed uncertainty measures holds on the requirements for an uncertainty measure. Finally evaluation of uncertainty in a DSmT-based localization problem is presented to show how to apply the generalized uncertainty measures, GAU1 and GAU2.

scholarly journals A novel complex fuzzy N-soft sets and their decision-making algorithm

2021 ◽  
Author(s):  
Tahir Mahmood ◽  
Ubaid ur Rehman ◽  
Zeeshan Ali
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Uncertain Information ◽  
The Novel ◽  
Soft Sets ◽  
Numerical Examples ◽  
Life Problems ◽  
Special Cases ◽  
Novel Concept

AbstractComplex fuzzy N-soft set (CFN-SS) is an important technique to manage awkward and unreliable information in realistic decision-making problems. CFN-SS is a blend of two separate theories, called N-soft sets (N-SSs) and complex fuzzy sets (CFSs), which are the modified versions of soft sets (SSs) and fuzzy sets (FSs) to depict vague and uncertain information in daily life problems. In this manuscript, the novel concept of CFN-SS is explored and their fundamental laws are discussed. CFN-SS contains the grade of truth in the form of a complex number whose real and imaginary parts are limited to the unit interval. Besides, we examine some algebraic properties for CFN-SS like union, intersections and justify these properties with the help of some numerical examples. To examine the superiority and effectiveness of the proposed approaches, the special cases of the investigated approaches are also discussed. A decision-making procedure is developed by using the investigated ideas based on CFN-SSs. Further, some numerical examples are also illustrated with the help of explored ideas to find the reliability and effectiveness of the proposed approaches. Finally, the comparative analysis of the investigated ideas with some existing ideas is also demonstrated to prove the quality of the proposed works. The graphical expressions of the obtained results are also discussed.

Our Bodies, Whose Property?

2013 ◽  
Author(s):  
Anne Phillips
Keyword(s):  
Labor Markets ◽  
Personal Autonomy ◽  
The Body ◽  
Body Parts ◽  
Complex Issue ◽  
Human Organs ◽  
Property Owners ◽  
The Common ◽  
The Difference ◽  
The Right

No one wants to be treated like an object, regarded as an item of property, or put up for sale. Yet many people frame personal autonomy in terms of self-ownership, representing themselves as property owners with the right to do as they wish with their bodies. Others do not use the language of property, but are similarly insistent on the rights of free individuals to decide for themselves whether to engage in commercial transactions for sex, reproduction, or organ sales. Drawing on analyses of rape, surrogacy, and markets in human organs, this book challenges notions of freedom based on ownership of our bodies and argues against the normalization of markets in bodily services and parts. The book explores the risks associated with metaphors of property and the reasons why the commodification of the body remains problematic. The book asks what is wrong with thinking of oneself as the owner of one's body? What is wrong with making our bodies available for rent or sale? What, if anything, is the difference between markets in sex, reproduction, or human body parts, and the other markets we commonly applaud? The book contends that body markets occupy the outer edges of a continuum that is, in some way, a feature of all labor markets. But it also emphasizes that we all have bodies, and considers the implications of this otherwise banal fact for equality. Bodies remind us of shared vulnerability, alerting us to the common experience of living as embodied beings in the same world. Examining the complex issue of body exceptionalism, the book demonstrates that treating the body as property makes human equality harder to comprehend.

Theory-Practice Gap: The Experiences of Nigerian Nursing Students

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Titilayo Dorothy Odetola ◽  
Olusola Oluwasola ◽  
Christoph Pimmer ◽  
Oluwafemi Dipeolu ◽  
Samson Oluwayemi Akande ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Nursing Students ◽  
Developing Country ◽  
The Body ◽  
Clinical Staff ◽  
Clinical Settings ◽  
Practice Gap ◽  
The Difference ◽  
Analyse Data ◽  
Theory Practice Gap

The “disconnect” between the body of knowledge acquired in classroom settings and the application of this knowledge in clinical practice is one of the main reasons for professional fear, anxiety and feelings of incompetence among freshly graduated nurses. While the phenomenon of the theory-to-practice gap has been researched quite extensively in high-income country settings much less is known about nursing students’ experiences in a developing country context. To rectify this shortcoming, the qualitative study investigated the experiences of nursing students in their attempt to apply what they learn in classrooms in clinical learning contexts in seven sites in Nigeria. Thematic content analysis was used to analyse data gained from eight focus group discussions (n = 80) with the students. The findings reveal a multifaceted theory-practice gap which plays out along four tensions: (1) procedural, i.e. the difference between practices from education institutions and the ones enacted in clinical wards – and contradictions that emerge even within one clinical setting; (2) political, i.e. conflicts that arise between students and clinical staff, especially personnel with a lower qualification profile than the degree that students pursue; (3) material, i.e. the disconnect between contemporary instruments and equipment available in schools and the lack thereof in clinical settings; and (4) temporal, i.e. restricted opportunities for supervised practice owing to time constraints in clinical settings in which education tends to be undervalued. Many of these aspects are linked to and aggravated by infrastructural limitations, which are typical for the setting of a developing country. Nursing students need to be prepared regarding how to deal with the identified procedural, political, material and temporal tensions before and while being immersed in clinical practice, and, in so doing, they need to be supported by educationally better qualified clinical staff.

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