Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion....

Efficient modeling of uncertain information in real world is still an open issue. Dempster-Shafer evidence theory is one of the most commonly used methods. However, the Dempster-Shafer evidence theory has the assumption that the hypothesis in the framework of discernment is exclusive of each other. This condition can be violated in real applications, especially in linguistic decision making sin...

Dempster-Shafer (DS) theory is formulated in terms of propositional logic, using the implicit notion of provability underlying DS theory. Dempster-Shafer theory can be modeled in terms of propositional logic by the tuple (~, p), where S is a set of propositional clauses and p is an assignment of mass to each clause Ei c ~. It is shown that the disjunction of minimal support clauses for a clause...

We present intrusion detection algorithms to detect physical layer jamming attacks in wireless networks. We compare the performance of local algorithms on the basis of the signal-to-interference-plus-noise ratio (SINR) executing independently at several monitors, with a collaborative detection algorithm that fuses the outputs provided by these algorithms. The local algorithms fall into two cate...

The aim of this paper is to show that Dempster–Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster–Shafer formulation allows to consider unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are genera...

The present state of development of Dempster-Shafer theory is surveyed and its place among theories of dealing with uncertainty in AI is discussed. No knowledge of the theory is assumed.

This papers discusses several implementation aspects for Dempster-Shafer belief functions. The main objective is to propose an appropriate representation of mass functions and efficient data structures and algorithms for the two basic operations of combination and marginalization.

This paper develops multiple-prior Bayesian inference for a set-identi…ed parameter whose identi…ed set is constructed by an intersection of two identi…ed sets. We formulate an econometrician’s practice of "adding an assumption" as "updating ambiguous beliefs." Among several ways to update ambiguous beliefs proposed in the literature, we consider the DempsterShafer updating rule (Dempster (1968...

Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and ea...

In this paper, we extend Smets’ transferable belief model (TBM) with probabilistic priors. Our first motivation for the extension is about evidential reasoning when the underlying prior knowledge base is Bayesian. We extend standard Dempster models with prior probabilities to represent beliefs and distinguish between two types of induced mass functions on an extended Dempster model: one for bel...