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Ensemble clustering consists in combining multiple clustering solutions into a single one, called the consensus, which can produce a more accurate and robust clustering of the data. In this paper, we attempt to implement ensemble clustering using Dempster-Shafer evidence theory. Individual clustering solutions are obtained using evidence theory and a novel diversity measure is proposed using the distance...
Neighborhood based classifiers are commonly used in the applications of pattern classification. However, in the implementation of neighborhood based classifiers, there always exist the problems of uncertainty. For example, when one use k-NN classifier, the parameter k should be determined, which can be big or small. Therefore, uncertainty problem occurs for the classification caused by the k value...
Image registration is a crucial and necessary step before image fusion. It aims to achieve the optimal match between two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. In the procedure of image registration, several types of uncertainty will be encountered, e.g., the selection of control points and the distance or the dissimilarity...
Multi-criteria decision making (MCDM) is to make decisions in the presence of multiple criteria. To make a decision in the framework of MCDM under uncertainty, a novel fuzzy — Cautious OWA with evidential reasoning (FCOWA-ER) approach is proposed in this paper. Payoff matrix and belief functions of states of nature are used to generate the expected payoffs, based on which, two Fuzzy Membership Functions...
The theory of belief function, also called Dempster-Shafer evidence theory, has been proved to be a very useful representation scheme for expert and other knowledge based systems. However, the computational complexity of evidence combination will become large with the increasing of the frame of discernment's cardinality. To reduce the computational cost of evidence combination, the idea of basic belief...
The dissimilarity of evidence, which represent the degree of dissimilarity between bodies of evidence (BOE's), has attracted more and more research interests and has been used in many applications based on evidence theory. In this paper, some novel dissimilarities of evidence are proposed by using fuzzy sets theory (FST). The basic belief assignments (bba's) are first transformed to the measures in...
In Dempster-Shafer Theory (DST) of evidence and transferable belief model (TBM), the probability transformation is necessary and crucial for decision-making. The evaluation of the quality of the probability transformation is usually based on the entropy or the probabilistic information content (PIC) measures, which are questioned in this paper. Another alternative of probability transformation approach...
Dempster rule of combination is an effective method in evidence reasoning; however, the original combination rule may produce counterintuitive results when there are severe conflicts among evidences due to the discarding of contradictory mass assignments. In this paper a modified combination rule is proposed based on ambiguity measure (AM), which can describe the evidencepsilas degree of uncertainty...
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