We revisit Zadeh's notion of "evidence the second kind" and show that it provides foundation for a general theory epistemic random fuzzy sets, which generalizes both Dempster-Shafer belief functions possibility theory. In this perspective, deals with generated by while induced sets. The more allows us to represent combine evidence is uncertain fuzzy. demonstrate application formalism statistica...

Dempster-Shafer evidence theory (D-S) is an effective instrument for merging the collected pieces of basic probability assignment (BPA), and it exhibits superiority in achieving robustness soft computing decision making uncertain imprecise environment. However, determination BPA still uncertain, merely applying can sometimes lead to counterintuitive results when lines conflict. In this paper, a...

In engineering scenarios, expert judgments play an essential role in reliability assessment, especially for those systems with few historical data. To achieve a rational result, experts from different areas should be involved, and the uncertainties their assessments properly addressed. Such information is often referred to as multisource imprecise (MSII) might contain high degree of conflicts, ...

china is one of the largest grain producing and consuming nations in the world and the importance of grain security to the chinese can never be overemphasized. in this paper, we present a comprehensive early-warning model for evaluating the status of grain security in china. the model is based on the analytic hierarchy process (ahp) method and the dempster–shafer theory (dst). we divided the ri...

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...

The conditioning in the Dempster-Shafer Theory of Evidence has been defined (by Shafer [15] as combination of a belief function and of an ”event” via Dempster rule. On the other hand Shafer [15] gives a ”probabilistic” interpretation of a belief function (hence indirectly its derivation from a sample). Given the fact that conditional probability distribution of a sample-derived probability dist...

Wireless sensor networks have increasingly become contributors of very large amounts of data. The recent deployment of wireless sensor networks in Smart City infrastructures have led to very large amounts of data being generated each day across a variety of domains, with applications including environmental monitoring, healthcare monitoring and transport monitoring. The information generated th...

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...