In this paper, we continue the theoretical and experimental analysis of two widely used combining rules, namely, the simple and weighted average of classifier outputs, that we started in previous works. We analyse and compare the conditions which affect the performance improvement achievable by weighted average over simple average, and over individual classifiers, under the assumption of unbias...

A large experiment on combining classifiers is reported and discussed. It includes, both, the combination of different classifiers on the same feature set and the combination of classifiers on different feature sets. Various fixed and trained combining rules are used. It is shown that there is no overall winning combining rule and that bad classifiers as well as bad feature sets may contain val...

In classifier combining, one tries to fuse the information that is given by a set of base classifiers. In such a process, one of the difficulties is how to deal with the variability between classifiers. Although various measures and many combining rules have been suggested in the past, the problem of constructing optimal combiners is still heavily studied. In this paper, we discuss and illustra...