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Deriving Evidence Theoretical Functions in Multivariate Data Spaces: A Systematic Approach

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wang, H and McClean, SI (2008) Deriving Evidence Theoretical Functions in Multivariate Data Spaces: A Systematic Approach. IEEE Transactions on Systems, Man, and Cybernetics Part B, 38(2) (2). pp. 455-465. [Journal article]

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URL: http://ieeexplore.ieee.org/ielx5/3477/4468809/04436072.pdf?arnumber=4436072

DOI: 10.1109/TSMCB.2007.913593

Abstract

The mathematical theory of evidence is a generalization of the Bayesian theory of probability. It is one of the primary tools for knowledge representation and uncertainty and probabilistic reasoning and has found many applications. Using this theory to solve a specific problem is critically dependent on the availability of a mass function (or basic belief assignment). In this paper, we consider the important problem of how to systematically derive mass functions from the common multivariate data spaces and also the ensuing problem of how to compute the various forms of belief function efficiently. We also consider how such a systematic approach can be used in practical pattern recognition problems. More specifically, we propose a novel method in which a mass function can be systematically derived from multivariate data and present new methods that exploit the algebraic structure of a multivariate data space to compute various belief functions including the belief, plausibility, and commonality functions in polynomial-time. We further consider the use of commonality as an equality check. We also develop a plausibility-based classifier. Experiments show that the equality checker and the classifier are comparable to state-of-the-art algorithms.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Information and Communication Engineering
ID Code:7587
Deposited By:Professor Sally McClean
Deposited On:20 Jan 2010 16:08
Last Modified:10 Jan 2012 15:15

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