Relaxing Feature Selection in Spam Filtering by Using Case-Based Reasoning Systems

  • J. R. Méndez
  • F. Fdez-Riverola
  • D. Glez-Peña
  • F. Díaz
  • J. M. Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4874)


This paper presents a comparison between two alternative strategies for addressing feature selection on a well known case-based reasoning spam filtering system called SpamHunting. We present the usage of the k more predictive features and a percentage-based strategy for the exploitation of our amount of information measure. Finally, we confirm the idea that the percentage feature selection method is more adequate for spam filtering domain.


Feature Selection Feature Selection Method Case Base Reasoning Concept Drift Feature Selection Approach 
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 2007

Authors and Affiliations

  • J. R. Méndez
    • 1
  • F. Fdez-Riverola
    • 1
  • D. Glez-Peña
    • 1
  • F. Díaz
    • 2
  • J. M. Corchado
    • 3
  1. 1.Dept. Informática, University of Vigo, Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004, OurenseSpain
  2. 2.Dept. Informática, University of Valladolid, Escuela Universitaria de Informática, Plaza Santa Eulalia, 9-11, 40005, SegoviaSpain
  3. 3.Dept. Informática y Automática, University of Salamanca, Plaza de la Merced s/n, 37008, SalamancaSpain

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