The Dempster-Shafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. In this paper a special case of combination of two bodies of evidence with non-zero conflict coefficient is considered. It is shown that application of the Dempster-Shafer rule of combination in this case leads to an evaluation of masses of the combined bodies that is ...

This paper describes a novel methodology for predicting fault prone modules. The methodology is based on Dempster-Shafer (D-S) belief networks. Our approach consists of three steps: First, building the Dempster-Shafer network by the induction algorithm; Second, selecting the predictors (attributes) by the logistic procedure; Third, feeding the predictors describing the modules of the current pr...

How to extend standard support vector machines to solve multi-class classification problem and yield the outputs in the frame of Dempster-Shafer theory is useful. The multi-class probability support vector machine is proposed, firstly. The Dempster-Shafer theory based multi-class support vector machine is designed by constructing probability support vector machines for binary classification usi...

We recall the existence of two methods for conditioning belief functions due to Dempster: one, known as Dempster conditioning, that applies Bayesian conditioning to the plausibility function and one that performs a sensitivity analysis on a conditional probability. We recall that while the first one is dedicated to revising a belief function, the other one is tailored to a prediction problem wh...

The most widely used updating rule for non-additive probabilities is the Dempster-Schafer rule. Schmeidler and Gilboa have developed a model of decision making under uncertainty based on non-additive probabilities, and in their paper "Updating Ambiguous Beliefs" they justify the Dempster-Schafer rule based on a maximum likelihood procedure. This note shows in the context of Schmeidler-Gilboa pr...

An algorithm for updating the evidence in the Dempster–Shafer theory is presented. The algorithm is based on an idea of indices. These indices are used to code the process of reasoning under uncertainty (the combination of evidence)using the Dempster-Shafer theory. The algorithm allows to carry out the reasoning with updating the evidence in much more efficient way than using the original Demps...

introduction environmental impact assessment is a systematic process to identify, predict and evaluate the environmental effects of proposed actions and projects. this process is applied prior to major decisions and commitments being made. environment, social, cultural and health effects are considered as an integral part of eia. particular attention is paid to eia practice for preventing, miti...

The Dempster-Shafer “theory of evidence” encompasses and extends the Bayes Theorem-based decision making machinery. Dempster-Shafer’s innovation is the introduction of lower and upper bounds, designated “belief” and “plausibility”, that are attached to probability estimates. The Dempster-Shafer algebra provides for propagation of and reasoning about these quantities according to an algebra whos...

The paper presents a novel view of the Dempster-Shafer belief function as a measure of diversity in relational data bases. The Dempster rule of evidence combination corresponds to the join operator of the relational database theory. This rough-set based interpretation is qualitative in nature and can represent a number of belief function operators.