There is a problem of applying of combining rules to evidence which were got from different information sources in framework of Dempster-Shafer theory. In this work the conflict measure and index of decreasing of ignorance in frame of Dempster-Shafer theory are introduced for characterization of quality of applied combining rules. Those functionals are analyzed on the bodies of evidences of spe...

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...

Combining outputs from different classifiers to achieve high accuracy in classification task is one of the most active research areas in ensemble method. Although many state-of-art approaches have been introduced, no method is outstanding compared with the others on numerous data sources. With the aim of introducing an effective classification model, we propose a Gaussian Mixture Model (GMM) ba...