Combining Classifiers through Triplet-Based Belief Functions

Authors:
Yaxin Bi;Shengli Wu;Xuhui Shen;Pan Xiong
Affiliations:
School of Computing and Mathematics, University of Ulster, Newtownabbey, Co. Antrim, UK BT37 0QB;School of Computing and Mathematics, University of Ulster, Newtownabbey, Co. Antrim, UK BT37 0QB;Institute of Earthquake Science, China Earthquake Administration, Beijing, China 100036;Institute of Earthquake Science, China Earthquake Administration, Beijing, China 100036
Venue:
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Year:
2008

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Abstract

Classifier outputs in the form of continuous values have often been combined using linear sum or stacking, but little is generally known about evidential reasoning methods for combining truncated lists of ordered decisions. In this paper we introduce a novel class-indifferent method for combining such a kind of classifier decisions. Specifically we model each output given by classifiers on new instances as a list of ranked decisions that is divided into 2 subsets of decisions, which are represented by triplet-based belief functionsand then are combined using Dempster's rule of combination. We present a formalism for triplet-based belief functions and establish a range of general formulae for combining these beliefs in order to arrive at a consensus decision. In addition we carry out a comparative analysis with an alternative representation dichotomous belief functionson the UCI benchmark data. We also compare our combination method with the popular methods of stacking, boosting, linear sum and majority voting over the same benchmark data to demonstrate the advantage of our approach.