MEE with Discrete Errors

  • Joaquim P. Marques de Sá
  • Luís M. A. Silva
  • Jorge M. F. Santos
  • Luís A. Alexandre
Part of the Studies in Computational Intelligence book series (SCI, volume 420)

Abstract

In this chapter we turn our attention to classifiers with a discrete error variable, E = T - Z. The need to operate with discrete errors arises when classifiers only produce a discrete output, as for instance the univariate data splitters used by decision trees. For regression-like classifiers, producing Z as a thresholding of a continuous output, Z = θ(Y), such a need does not arise. The present analysis of MEE with discrete errors, besides complementing our understanding of EE-based classifiers will also serve to lay the foundations of EE-based decision trees later in the chapter.

Keywords

Information Gain Gini Index Discrete Error Probability Mass Function Split Point 
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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Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • Joaquim P. Marques de Sá
    • 1
  • Luís M. A. Silva
    • 2
  • Jorge M. F. Santos
    • 3
  • Luís A. Alexandre
    • 4
  1. 1.Divisão de Sinal e Imagem Campus FEUPINEB-Instituto de Engenharia BiomédicaPortoPortugal
  2. 2.Dept. of MathematicsUniv. de AveiroAveiroPortugal
  3. 3.Dept. of MathematicsISEP, School of Engineering Polytechnic of PortoPortoPortugal
  4. 4.Dept. of InformaticsUniv. Beira Interior IT - Instituto de TelecomunicaçõesCovilhãPortugal

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