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Fast-BR vs. Fast-CT_EXT: An Empirical Performance Study

  • Vladímir Rodríguez-DiezEmail author
  • José Fco. Martínez-Trinidad
  • J. Ariel Carrasco-Ochoa
  • Manuel S. Lazo-Cortés
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10267)

Abstract

Testor Theory allows performing feature selection in supervised classification problems through typical testors. Typical testors are irreducible subsets of features preserving the object discernibility ability of the original set of features. However, finding the complete set of typical testors for a dataset requires a high computational effort. In this paper, we make an empirical study about the performance of two of the most recent and fastest algorithms of the state of the art for computing typical testors, regarding the density of the basic matrix. For our study we use synthetic basic matrices to control their characteristics, but we also include public standard datasets taken from the UCI machine learning repository. Finally, we discuss our conclusions drawn from this study.

Keywords

Testor theory Algorithms Basic matrix 

Notes

Acknowledgements

This work was partly supported by National Council of Science and Technology of Mexico under the scholarship grant 399547.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vladímir Rodríguez-Diez
    • 1
    • 2
    Email author
  • José Fco. Martínez-Trinidad
    • 1
  • J. Ariel Carrasco-Ochoa
    • 1
  • Manuel S. Lazo-Cortés
    • 1
  1. 1.Coordinación de Ciencias ComputacionalesInstituto Nacional de Astrofísica, Óptica y ElectrónicaTonantzintlaMexico
  2. 2.Universidad de CamagüeyCamagüeyCuba

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