Applying Cost-Sensitive Multiobjective Genetic Programming to Feature Extraction for Spam E-mail Filtering

  • Yang Zhang
  • HongYu Li
  • Mahesan Niranjan
  • Peter Rockett
Conference paper

DOI: 10.1007/978-3-540-78671-9_28

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4971)
Cite this paper as:
Zhang Y., Li H., Niranjan M., Rockett P. (2008) Applying Cost-Sensitive Multiobjective Genetic Programming to Feature Extraction for Spam E-mail Filtering. In: O’Neill M. et al. (eds) Genetic Programming. EuroGP 2008. Lecture Notes in Computer Science, vol 4971. Springer, Berlin, Heidelberg

Abstract

In this paper we apply multiobjective genetic programming to the cost-sensitive classification task of labelling spam e-mails. We consider three publicly-available spam corpora and make comparison with both support vector machines and naïve Bayes classifiers, both of which are held to perform well on the spam filtering problem. We find that for the high cost ratios of practical interest, our cost-sensitive multiobjective genetic programming gives the best results across a range of performance measures.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yang Zhang
    • 1
  • HongYu Li
    • 2
  • Mahesan Niranjan
    • 2
  • Peter Rockett
    • 1
  1. 1.Laboratory for Information and Vision Engineering, Department of Electronic and Electrical EngineeringThe University of SheffieldSheffieldUK
  2. 2.Department of Computer ScienceThe University of SheffieldSheffieldUK

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