Pairwise classifier combination using belief functions

Authors:
Benjamin Quost;Thierry Denux;Marie-Hélène Masson
Affiliations:
UMR CNRS 6599 Heudiasyc, Université de Technologie de Compiègne, BP 20529, F-60205 Compiègne cedex, France;UMR CNRS 6599 Heudiasyc, Université de Technologie de Compiègne, BP 20529, F-60205 Compiègne cedex, France;UMR CNRS 6599 Heudiasyc, Université de Technologie de Compiègne, BP 20529, F-60205 Compiègne cedex, France
Venue:
Pattern Recognition Letters
Year:
2007

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Abstract

In the so-called pairwise approach to polychotomous classification, a multiclass problem is solved by combining classifiers trained to discriminate between each pair of classes. In this paper, this approach is revisited in the framework of the Dempster-Shafer theory of belief functions, a non-probabilistic framework for quantifying and manipulating partial knowledge. It is proposed to interpret the output of each pairwise classifiers by a conditional belief function. The problem of classifier combination then amounts to computing the non-conditional belief function which is the most consistent, according to some criterion, with the conditional belief functions provided by the classifiers. Experiments with various datasets demonstrate the good performances of this method as compared to previous approaches to the same problem.