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Application of Feature Selection to Real Problems

  • Verónica Bolón-Canedo
  • Noelia Sánchez-Maroño
  • Amparo Alonso-Betanzos
Part of the Artificial Intelligence: Foundations, Theory, and Algorithms book series (AIFTA)

Abstract

This chapter presents two real problems where feature selection has proved to be useful in improving performance. Section 5.1 is devoted to improving the classification accuracy of the KDD Cup 99 dataset, a benchmark in the intrusion detection field. A method based on the combination of discretization, filtering and classification algorithms is proposed, to be applied to both the binary and the multiclass version of the dataset. Then, the second problem is presented in Section 5.2, which is related to tear film lipid layer classification. A methodology making use of feature selection methods is proposed, achieving considerable improvements in performance. Since the second problem has the added handicap of having to reduce the cost associated with the features, in Section 5.3 we introduce a general framework for cost-based feature selection. Section 5.4 closes this chapter with a summary of its findings.

Keywords

Feature Selection Pareto Front Intrusion Detection Discrete Wavelet Feature Selection Method 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Verónica Bolón-Canedo
    • 1
  • Noelia Sánchez-Maroño
    • 1
  • Amparo Alonso-Betanzos
    • 1
  1. 1.Facultad de InformáticaUniversidad de A CoruñaA CoruñaSpain

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