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A New Disagreement Measure for Characterization of Classification Problems

  • Yulia Ledeneva
  • René Arnulfo García-Hernández
  • Alexander Gelbukh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9142)

Abstract

Robert P.W. Duin, Elzbieta Pekalska and David M.J. Tax proposed the characterization of classification problems by classifier disagreement. They showed that it is possible to use a standard set of supervised classification problems for constructing a rule that allows deciding about the similarity of new problems to the existing ones. The classifier disagreement could be used to group classification problems in a way which could help to select the appropriate tools for solving new problems. Duin et al proposed a dissimilarity measure between two problems taking into account only the full disagreement matrices. They used a measure of the disagreement based on the coincidence of the classifier output however the correctness was not considered. In this work, we propose a new measure of disagreement which takes into account the correctness of classification result. To calculate the disagreement each object is analyzed to verify if it was classified correctly or incorrectly by the classifiers. We use this new disagreement measure to calculate the dissimilarity between two problems. Some experiments were done and the results were compared against Duin’s et al results.

Keywords

Classification Result Classification Problem Logistic Classifier Classifier Disagreement Dissimilarity Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Duin, R.P.W., Pekalska, E., Tax, David M.J.: The Characterization of Classification Problems by Classifier Disagreement. In: Proceedings of the ICPR 2004, in CD (2004)Google Scholar
  2. 2.
    Blake, C.L., Merz, C.J., UCI Repository of machine learning databases. Univ. of California, Irvine, CA (1998). http://www.ics.uci.edu/~mlearn/MLRe-pository.html
  3. 3.
    Duin, R.P.W., Juszczak, P., Paclik, P., Pekalska, E., Ridder, D., Tax, D.M.J., PRTools4, a Matlab toolbox for Pattern Recognition. Delft Univ. of Techn. (2004). http://prtools.org

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yulia Ledeneva
    • 1
  • René Arnulfo García-Hernández
    • 1
  • Alexander Gelbukh
    • 2
  1. 1.Autonomous University of the State of Mexico, Instituto Literario 100TolucaMexico
  2. 2.Natural Language and Text Processing LaboratoryCenter for Computing Research National Polytechnic InstituteMexicoMexico

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