Feature Selection Using Typical ε: Testors, Working on Dynamical Data

  • Jesús Ariel Carrasco-Ochoa
  • José Ruiz-Shulcloper
  • Lucía Angélica De-la-Vega-Doría
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

Typical e:testors are useful to do feature selection in supervised classification problems with mixed incomplete data, where similarity function is not the total coincidence, but it is a one threshold function. In this kind of problems, modifications on the training matrix can appear very frequently. Any modification of the training matrix can change the set of all typical ε:testors, so this set must be recomputed after each modification. But, complexity of algorithms for calculating all typical ε:testors of a training matrix is too high. In this paper we analyze how the set of all typical ε:testors changes after modifications. An alternative method to calculate all typical ε:testors of the modified training matrix is exposed. The new method’s complexity is analyzed and some experimental results are shown.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jesús Ariel Carrasco-Ochoa
    • 1
  • José Ruiz-Shulcloper
    • 2
  • Lucía Angélica De-la-Vega-Doría
    • 1
  1. 1.Computer Science DepartmentNational Institute of Astrophysics, Optics and ElectronicsSta María TonanzintlaMexico
  2. 2.Advanced Technologies and Application CenterMINBAS (Cuba) 

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