A Fast Approach to Improve Classification Performance of ECOC Classification Systems

  • Paolo Simeone
  • David M. J. Tax
  • Robert P. W. Duin
  • Francesco Tortorella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)


Error correcting output coding is a well known technique to decompose a multi-class classification problem into a group of two-class problems which can be faced by using a combination of binary classifiers. Each of them is trained on a different dichotomy of the classes. The way the set of classes is mapped on this set of dichotomies may essentially influence the obtained performance. In this paper we present a new tool, the k-NN lookup table to optimize this mapping in a fast way and a fast procedure to change the dichotomies in a proper way. Experiments on artificial and public data sets show that the proposed procedure may significantly improve the ECOC performance in multi-class problems.


multiple classifiers systems ECOC k-NN  


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Paolo Simeone
    • 1
  • David M. J. Tax
    • 2
  • Robert P. W. Duin
    • 2
  • Francesco Tortorella
    • 1
  1. 1.DAEIMIUniversità degli Studi di CassinoCassino (FR)Italy
  2. 2.Information and Communication Theory GroupDelft University of TechnologyDelftThe Netherlands

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