The Dempster-Shafer theory of belief functions is an important approach to deal with uncertainty in AI. In the theory, belief functions are defined on Boolean algebras of events. In many applications of belief functions in real world problems, however, the objects that we manipulate is no more a Boolean algebra but a distributive lattice. In this paper, we extend the Dempster-Shafer theory to t...

Dempster-Shafer evidence theory has been extensively used in many information fusion systems since it was proposed by Dempster and extended by Shafer. Many scholars have been conducted on conflict management of Dempster-Shafer evidence theory in past decades. However, how to determine a potent parameter to measure evidence conflict, when the given environment is in an open world, namely the fra...

Based on World Health Organization (WHO) report in the 2011 Skin diseases still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin diseases are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pai...

We present a Gibbs sampler for the Dempster–Shafer (DS) approach to statistical inference categorical distributions. The DS framework extends Bayesian approach, allows in particular use...

In this paper, speaker identification using the Dempster-Shafer theory of evidence is discussed. The objective is to use the complementary information present from different classifiers to fuse the classification results into a single decision. Here, we use a decreasing function of the distance (of the classifiers) as our belief function. In the case of speaker identification, we show that a co...

How to manage conflict is still an open issue in Dempster-Shafer evidence theory. The correlation coefficient can be used to measure the similarity of evidence in Dempster-Shafer evidence theory. However, existing correlation coefficients of belief functions have some shortcomings. In this paper, a new correlation coefficient is proposed with many desirable properties. One of its applications i...

This contribution deals with a belief processing which enables managing of multiple and overlapping elements of a frame of discernment. An outline of the Dempster-Shafer theory for such cases is presented, including several types of constraints for simplification of its large computational complexity. DSmT — a new theory rapidly developing the last five years — is briefly introduced. Finally, i...

In this paper, target differentiation based on pattern of data which are obtained by a set of two ultrasonic sensors is considered. A neural network based target classifier is applied to these data to categorize the data of each sensor. Then the results are fused together by Dempster–Shafer theory (DST) and Dezert–Smarandache theory (DSmT) to make final decision. The Generalized Aggregated Unce...

Software Quality and Reliability Prediction Using Dempster-Shafer Theory

In this comment we discuss relative strengths and weaknesses of simplex Dirichlet Dempster-Shafer inference as applied to multi-resolution tests independence.