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Combining Evidence from Classifiers in Text Categorization

Bi, Yaxin, Bell, David A. and Guan, Jiwen (2004) Combining Evidence from Classifiers in Text Categorization. In: Knowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 521-528. ISBN 978-3-540-23205-6 [Book section]

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Abstract

In this paper, we describe a way for modelling a generalization process involved in the combination of multiple classification systems as an evidential reasoning process. We first propose a novel structure for representing multiple pieces of evidence de‘rived from multiple classifiers. This structure is called a focal element triplet. We then present a method for combining multiple pieces of evidence by using Dempster’s rule of combination. The advantage of the novel structure is that it not only facilitates the distinguishing of trivial focal elements from important ones, but it also reduces the effective computation-time from exponential as in the conventional process of combining multiple pieces of evidence to linear. In consequence, this allows Dempster’s rule of combination to be implemented in a widened range of applications.

Item Type:Book section
Faculties and Schools:Faculty of Computing & 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
ID Code:25517
Deposited By: Dr Yaxin Bi
Deposited On:20 Jan 2016 15:37
Last Modified:20 Jan 2016 15:37

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