The Multi-Ranked Classifiers Comparison

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


Is it true that everybody knows how to compare classifiers in terms of reliability? Probably not, since it is so common that just after reading a paper we feel that the classifiers’ performance analysis is not exhaustive and we would like to see more information or more trustworthy information. The goal of this paper is to propose a method of multi-classifier comparison on several benchmark data sets. The proposed method is trustworthy, deeper, and more informative (multi-aspect). Thanks to this method, we can see much more than overall performance. Today, we need methods which not only answer the question whether a given method is the best, because it almost never is. Apart from the general strength assessment of a learning machine we need to know when (and whether) its performance is outstanding or whether its performance is unique.


  1. 1.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. Inst. Electr. Electron. Eng. Trans. Inf. Theory 13(1), 21–27 (1967)zbMATHGoogle Scholar
  2. 2.
    Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)CrossRefGoogle Scholar
  3. 3.
    Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2001)zbMATHGoogle Scholar
  4. 4.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: International Joint Conference on Neural Networks, pp. 985–990. IEEE Press (2004)Google Scholar
  5. 5.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)CrossRefGoogle Scholar
  6. 6.
    Larose, D.: Discovering Knowledge in Data. An Introduction to Data Mining. Wiley, New York (2005)zbMATHGoogle Scholar
  7. 7.
    Merz, C.J., Murphy, P.M.: UCI repository of machine learning databases (1998).
  8. 8.
    Montgomery, D.C., Runger, G.C.: Applied Statistics and Probability for Engineers. Wiley, New York (2002)zbMATHGoogle Scholar
  9. 9.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of InformaticsNicolaus Copernicus UniversityToruńPoland

Personalised recommendations