In Dempster-Shafer theory, belief functions induced by distinct pieces of evidence can be combined by Dempster's rule of combination. The concept of distinctness has not been formally defined. We present a tentative definition of the concept of distinctness and compare this definition with the definition of stochastic independence described in probability theory.

This paper addresses a soft computing approach of fusion of signals from different independent sources. The signals may be from different types of primary classifiers. The Dempster Shafer Evidence Accumulation (DSEA) theory provides a robust platform for evidence fusion and it incorporates uncertainty, imprecision and conflicting situations in the process of decision making into a mathematical ...

In this paper a new method for image retrieval using high level color semantic features is proposed. It is based on extraction of low level color characteristics and their conversion into high level semantic features using Johannes Itten theory of color, Dempster-Shafer theory of evidence and fuzzy production rules.

As a generalization of Dempster-Shafer theory, the theory of D numbers is a new theoretical framework for uncertainty reasoning. Measuring the uncertainty of knowledge or information represented by D numbers is an unsolved issue in that theory. In this paper, inspired by distance based uncertainty measures for Dempster-Shafer theory, a total uncertainty measure for a D number is proposed based ...

The semantics of similarity measures is studied and reduced to the evidence theory of Dempster and Shafer. Applications are given for classification and configuration, the latter uses utility theory in addition. In: Mathematical and Statistical Methods in Artificial Intelligence (eds. G. della Riccia, R. Kruse, R. Viertl), Springer Verlag 1995, pp. 171-184

This paper explains our proposed descriptive decision-making model under uncertainty elucidating different decision attitudes of humans. In this decision-making model, Dempster-Shafer Theory (DST) is used to represent uncertainty of the problem and the uncertainty is approximated by a probability distribution depending on the decision-makers’ attitude towards uncertainty. Prospect theory is the...

This paper presents a learning Dempster-Shafer model for the detection of buildings in aerial image and range data. The process of evidence assignment in the Dempster-Shafer method is implemented through membership functions in an adaptivenetwork-based fuzzy inference system, where a back propagation learning rule is employed to tune the evidence assignment functions using training samples. The...

Dempster-Shafer theory of evidence (D-S theory) is widely used in uncertain information process. The basic probability assignment(BPA) is a key element in D-S theory. How to measure the distance between two BPAs is an open issue. In this paper, a new method to measure the distance of two BPAs is proposed. The proposed method is a generalized of existing evidence distance. Numerical examples are...