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Discriminant Function Selection in Binary Classification Task

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)

Abstract

The ensemble selection is one of the important problems in building multiple classifier systems (MCSs). This paper presents dynamic ensemble selection based on the analysis of discriminant functions. The idea of the selection is presented on the basis of binary classification tasks. The paper presents two approaches: one takes into account the normalization of the discrimination functions, and in the second approach, normalization is not performed. The reported results based on the data sets form the UCI repository show that the proposed ensemble selection is a promising method for the development of MCSs.

Keywords

Ensemble selection Multiple classifier system Binary classification task 
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Notes

Acknowledgments

This work was supported by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264 and by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Technology.

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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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