A conceptual experimental framework for 0-theory m d the possibility form of fuzzy set theory is presented. The framework is probabilistic and derived from experimental measurements of evidential support. It is used to quantify the probabilistic masses needed in 0-theory and the membership functions used in fuzzy set theory. Instances in which these two different representations of the same exp...

This paper presents a novel decision-based fuzzy filter based on support vector machines and Dempster-Shafer evidence theory for effective noise suppression and detail preservation. The proposed filter uses an SVM impulse detector to judge whether an input pixel is noisy. Sources of evidence are extracted, and then the fusion of evidence based on the evidence theory provides a feature vector th...

This paper presents a clinical diagnosis support system which combines the advantages of Dempster-Shafer theory with Bayesian networks in order to simulate the uncertain medical reasoning. We propose a hierarchical structure using Dempster-Shafer at the upper level for evaluating more general hypothesis (disease groups) and Bayesian networks at the lower level for a more accurate analysis of sp...

According to the Multi-criteria Decision-making (MCDM) problem with criterion value for triangular fuzzy number, a kind of a MCDM method based on fuzzy number and Dempster-Shafer evidence theory is proposed. By means of the ability to deal with the uncertain information of evidence theory, the method first constructs the evidence body and the Basic Probability Assignment (BPA) function. In the ...

A simple Monte-Carlo algorithm can be used to calculate Dempster-Shafer belief very efficiently unless the conflict between the evidences is very high. This paper introduces and explores Markov Chain Monte-Carlo algorithms for calculating Dempster-Shafer belief that can also work well when the conflict is high.

The main object of the research work is to compare and correlate the Intelligent control which has a class of control technique used in several artificial intelligence computing approaches namely Motion or Path Planning (using potential fields),Evidence Theory (dempster – shafer),Fuzzy Systems, Neural Networks etc. A detailed study to arrive a research based solutions to find relations among th...

This reply to papers by Pearl and Shafer focuses on two issues underlying the debate on the validity o f using Dempster-Shafer theory, namely the requirement of a process-independent semantics and the a priori need for multiple uncertainty calculi. Pearl shows deficiencies of Dempster-Shafer theory in dealing with several instances o f commonsense reasoning in a process-independent manner. Alth...

In this paper, an extended ranking method for fuzzy numbers, which is a synthesis of fuzzy targets and the Dempster–Shafer Theory (DST) of evidence, is devised. The use of fuzzy targets to reflect human viewpoints in fuzzy ranking is not new. However, different fuzzy targets can lead to contradictory fuzzy ranking results; making it difficult to reach a final decision. In this paper, the result...

Evidence Theory is an important tool of uncertainty modelling when both epistemic and aleatory uncertainties are present in the problem under consideration. In the absence of empirical data, experts in related fields provide necessary information. The fundamental objects of this theory of evidence are called focal elements, and the primitive function associated with it is called basic probabili...