This paper develops a new uncertainty measure for the theory of hints that complies with the established semantics of statistical information theory and further satisfies all classical requirements for such a measure imposed in the literature. The proposed functional decomposes into conversant uncertainty measures and therefore discloses a new interpretation of the latters as well. By abstracti...

The goal of this paper is to study the connection between Dempster-Shafer theory and probabilistic argumentation systems. By introducing a general method to translate probabilistic argumentation systems into corresponding Dempster-Shafer belief potentials, its contribution is twofold. On the one hand, the paper proposes probabilistic argumentation systems as a convenient and powerful modeling l...

Automated ontology population using information extraction algorithms can produce inconsistent knowledge bases. Confidence values assigned by the extraction algorithms may serve as evidence helping to repair produced inconsistencies. The Dempster-Shafer theory of evidence is a formalism, which allows appropriate interpretation of extractors’ confidence values. The paper presents an algorithm fo...

The goal of this paper is to study the connection between Dempster-Shafer theory and probabilistic argumentation systems. By introducing a general method to translate probabilistic argumentation systems into corresponding Dempster-Shafer belief potentials, its contribution is twofold. On the one hand, the paper proposes probabilistic argumentation systems as a convenient and powerful modeling l...

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...

Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. DempsterShafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This...

Dempster-Shafer evidence theory is an efficient mathematical tool to deal with uncertain information. In that theory, basic probability assignment (BPA) is the basic element for the expression and inference of uncertainty. Decision-making based on BPA is still an open issue in Dempster-Shafer evidence theory. In this paper, a novel approach of transforming basic probability assignments to proba...

Dempster-Shafer theory is widely applied to uncertainty modelling and knowledge reasoning due to its ability of expressing uncertain information. However, some conditions, such as exclusiveness hypothesis and completeness constraint, limit its development and application to a large extend. To overcome these shortcomings in Dempster-Shafer theory and enhance its capability of representing uncert...

Finding defects in software is a challenging and time and budget consuming task. Minimizing these adverse effects using software defect prediction models via guiding testers with defective parts of software system is an attractive research area. Previous research emphasized the value of these tools with a mean probability of detection of 71 percent and mean false alarm rates of 25 percent. This...

Multi-classifier combination based on Dempster-Shafer theory of evidence has demonstrated it’s superior performance. In the approach based on Dempster-Shafer theory, the basic probability assignments for evidence are usually derived from classifiers’ global performance. However, our study discovered that while using classifiers’ global performance as basic probability assignments doesn’t necess...