Combining Evidence from Classifiers in Text Categorization

Yaxin Bi, David A. Bell, Jiwen Guan

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science
PublisherSpringer
Pages521-528
ISBN (Print)978-3-540-23205-6
Publication statusPublished - 2004

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  • Cite this

    Bi, Y., Bell, D. A., & Guan, J. (2004). Combining Evidence from Classifiers in Text Categorization. In Knowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science (pp. 521-528). Springer.