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Detecting Features from Confusion Matrices Using Generalized Formal Concept Analysis

  • Carmen Peláez-Moreno
  • Francisco J. Valverde-Albacete
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

We claim that the confusion matrices of multiclass problems can be analyzed by means of a generalization of Formal Concept Analysis to obtain symbolic information about the feature sets of the underlying classification task. We prove our claims by analyzing the confusion matrices of human speech perception experiments and comparing our results to those elicited by experts.

Keywords

Confusion Matrix Concept Lattice Confusion Matrice Formal Concept Analysis Galois Connection 
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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Carmen Peláez-Moreno
    • 1
  • Francisco J. Valverde-Albacete
    • 1
  1. 1.Dpto. de Teoría de la Señal y de las ComunicacionesUniversidad Carlos III de MadridLeganésSpain

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