Fractional Programming Weighted Decoding for Error-Correcting Output Codes

  • Firat Ismailoglu
  • I. G. Sprinkhuizen-Kuyper
  • Evgueni Smirnov
  • Sergio Escalera
  • Ralf Peeters
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9132)


In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Firat Ismailoglu
    • 1
  • I. G. Sprinkhuizen-Kuyper
    • 2
  • Evgueni Smirnov
    • 1
  • Sergio Escalera
    • 3
  • Ralf Peeters
    • 1
  1. 1.Maastricht UniversityDepartment of Knowledge EngineeringMaastrichtThe Netherlands
  2. 2.Radboud University NijmegenDonders Institute for Brain, Cognition and Behaviour, Centre for CognitionNijmegenThe Netherlands
  3. 3.University of Barcelona and Computer Vision CenterBarcelonaSpain

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