The combination of multiple classifiers using an evidential reasoning approach

In many domains when we have several competing classifiers available we want tosynthesize them or some of them to get a more accurate classifier by a combinationfunction. In this paper we propose a ‘class-indifferent’ method for combining classifierdecisions represented by evidential structures called triplet and quartet, using Dempster’srule of combination. This method is unique in that it distinguishes important elementsfrom the trivial ones in representing classifier decisions, makes use of more informationthan others in calculating the support for class labels and provides a practical way toapply the theoretically appealing Dempster–Shafer theory of evidence to the problem ofensemble learning. We present a formalism for modelling classifier decisions as tripletmass functions and we establish a range of formulae for combining these mass functionsin order to arrive at a consensus decision. In addition we carry out a comparativestudy with the alternatives of simplet and dichotomous structure and also compare twocombination methods, Dempster’s rule and majority voting, over the UCI benchmark data,to demonstrate the advantage our approach offers.