The Combination of Multiple Classifiers Using an Evidential Reasoning Approach

Yaxin Bi, Jiwen Guan, David Bell

Research output: Contribution to journalArticlepeer-review

91 Citations (Scopus)


In many domains when we have several competing classifiers available we want to synthesize them or some of them to get a more accurate classifier by a combination function. In this paper we propose a ‘class-indifferent’ method for combining classifier decisions represented by evidential structures called triplet and quartet, using Dempster's rule of combination. This method is unique in that it distinguishes important elements from the trivial ones in representing classifier decisions, makes use of more information than others in calculating the support for class labels and provides a practical way to apply the theoretically appealing Dempster–Shafer theory of evidence to the problem of ensemble learning. We present a formalism for modelling classifier decisions as triplet mass functions and we establish a range of formulae for combining these mass functions in order to arrive at a consensus decision. In addition we carry out a comparative study with the alternatives of simplet and dichotomous structure and also compare two combination methods, Dempster's rule and majority voting, over the UCI benchmark data, to demonstrate the advantage our approach offers. (A continuation of the work in this area that was published in IEEE Trans on KDE, and conferences)
Original languageEnglish
Pages (from-to)1731-1751
Number of pages21
JournalArtificial Intelligence
Issue number15
Publication statusPublished - Oct 2008

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics


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