scholarly journals Uncertain Portfolio Optimization based on Dempster-Shafer Theory

2021 ◽  
Author(s):  
Amir Hossein Eskorouchi
Keyword(s):  
Real Life ◽  
High Sensitivity ◽  
Dempster Shafer Theory ◽  
Exchange Rate Fluctuations ◽  
Two Factors ◽  
Unified View ◽  
Financial Decision ◽  
Shafer Theory ◽  
Portfolio Selection Model

Nowadays, the selection and management of the optimal portfolio are the most primary fields of financial decision-making. Thereby, selecting a portfolio capable of providing the highest efficiency and, at the same time, the lowest investment risk has been turned into one of the most critical concerns among financial activists. However, in this selection, the two factors above are not the only determining ones. Various factors are affecting financial markets' behavior under different possible scenarios, which should be identified. In this paper, we examine the high sensitivity of the Iranian capital market to the exchange rate fluctuations in the different scenarios due to the lack of a unified view of the value of that rate among experts as one of the mentioned factors and obtain its value using Dempster–Shafer theory (DST). Then, a portfolio selection model that prefers stocks with higher ranks is proposed. Representative results of the real-life case study reveal that the submitted approach is productive and practically applicable.

The Effect of Lesion Characteristic on Remineralization and Model Sensitivity

1992 ◽  
Vol 71 (3_suppl) ◽  
pp. 811-813 ◽  
Author(s):  
F. Schäfer ◽  
S.J. Raven ◽  
T.A. Parr
Keyword(s):  
Real Life ◽  
High Sensitivity ◽  
Lesion Size ◽  
Experimental Conditions ◽  
Treatment Groups ◽  
Human Enamel ◽  
Enamel Sample ◽  
Two Factors ◽  
Treatment Bias

A major criterion for assessing the value of any experimental model in scientific research is the degree of correspondence between its results and data from the real-life process it is designed to model. Intra-oral models aimed at predicting the anti-caries efficacy of toothpastes or other topical treatments should therefore be calibrated against treatments proven to be effective in a caries clinical trial. For this to be achieved, it is necessary that a model with high sensitivity be designed, while at the same time retaining relevance to the process to be modeled. This means that the effects of the various experimental conditions and parameters of the model on its performance must be understood. The purpose of this paper was to assess the influence of two specific factors on the performance of an in situ enamel remineralization model, which is based on human enamel slabs attached to partial dentures. The two factors are initial lesion severity and origin of enamel sample. The results indicated that initial lesion size affected whether net remineralization or net demineralization occurred during in situ treatment. Samples with an initial range of from 1500 to 2500 (ΔZ) tended more toward demineralization than did samples with ΔZ > 3500. This means that treatment groups must be well-balanced with respect to initial lesion size. Differences in initial demineralization severity between different tooth locations must also be considered so that systematic treatment bias can be avoided. The solution used in the model discussed here is based on a balanced experimental design, which allows this effect to be taken into account in the data analysis.

Uncertainty Analysis in Structural Dynamics

2013 ◽  
Vol 588 ◽  
pp. 318-332
Author(s):  
Keith Worden
Keyword(s):  
Uncertainty Analysis ◽  
Structural Dynamics ◽  
Evidence Based ◽  
Dempster Shafer Theory ◽  
Aerospace Structure ◽  
Uncertainty Representation ◽  
Neural Network Classifiers ◽  
The Many ◽  
Shafer Theory

This paper discusses the main issues of Uncertainty Analysis (UA) in general and also argues and illustrates its particular relevance to structural dynamics. Brief descriptions are given of the most prevalent of the many frameworks for uncertainty representation. The three main uncertainty-related problems of relevance to structural dynamics are then discussed, namelyquantification,fusionandpropagation. In order to illustrate the application of ideas of UA in a realistic scenario, there then follows a case study conducted on an aerospace structure, namely the wing of a Gnat trainer aircraft. The case study considers evidence-based classifiers as an alternative to probabilistic classifiers for the problem of damage location within the context of Structural Health Monitoring. Dempster-Shafer theory is employed to construct neural network classifiers with the potential to admit ignorance, rather than misclassify.

scholarly journals A novel weighted evidence combination rule based on improved entropy function with a diagnosis application

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882399 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang
Keyword(s):  
Medical Diagnosis ◽  
Real Life ◽  
Entropy Function ◽  
New Method ◽  
Combination Rule ◽  
Initial Weight ◽  
Rule Based ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Shafer Theory

Managing conflict in Dempster–Shafer theory is a popular topic. In this article, we propose a novel weighted evidence combination rule based on improved entropy function. This newly proposed approach can be mainly divided into two steps. First, the initial weight will be determined on the basis of the distance of evidence. Then, this initial weight will be modified using improved entropy function. This new method converges faster when handling high conflicting evidences and greatly reduces uncertainty of decisions, which can be demonstrated by a numerical example where the belief degree is raised up to 0.9939 when five evidences are in conflict, an application in faulty diagnosis where belief degree is increased hugely from 0.8899 to 0.9416 when compared with our previous works, and a real-life medical diagnosis application where the uncertainty of decision is reduced to nearly 0 and the belief degree is raised up to 0.9989.

Uncertain Reasoning for Detection of Selling Stolen Goods in Online Auctions Using Contextual Information

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Ladislav Beranek
Keyword(s):  
Contextual Information ◽  
Online Auctions ◽  
Uncertain Reasoning ◽  
Multiple Sources ◽  
Additional Information ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Stolen Goods

This work describes the design of a decision support system for detection of fraudulent behavior of selling stolen goods in online auctions. In this system, each seller is associated with a type of certification, namely “proper seller,” “suspect seller,” and “selling stolen goods.” The certification level is determined on the basis of a seller’s behaviors and especially on the basis of contextual information whose origin is outside online auctions portals. In this paper, we focus on representing knowledge about sellers in online auctions, the influence of additional information available from other Internet source, and reasoning on bidders’ trustworthiness under uncertainties using Dempster-Shafer theory of evidence. To demonstrate the practicability of our approach, we performed a case study using real auction data from Czech auction portal Aukro. The analysis results show that our approach can be used to detect selling stolen goods. By applying Dempster-Shafer theory to combine multiple sources of evidence for the detection of this fraudulent behavior, the proposed approach can reduce the number of false positive results in comparison to approaches using a single source of evidence.

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