Filter Methods for Feature Selection – A Comparative Study

  • Noelia Sánchez-Maroño
  • Amparo Alonso-Betanzos
  • María Tombilla-Sanromán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


Adequate selection of features may improve accuracy and efficiency of classifier methods. There are two main approaches for feature selection: wrapper methods, in which the features are selected using the classifier, and filter methods, in which the selection of features is independent of the classifier used. Although the wrapper approach may obtain better performances, it requires greater computational resources. For this reason, lately a new paradigm, hybrid approach, that combines both filter and wrapper methods has emerged. One of its problems is to select the filter method that gives the best relevance index for each case, and this is not an easy to solve question. Different approaches to relevance evaluation lead to a large number of indices for ranking and selection. In this paper, several filter methods are applied over artificial data sets with different number of relevant features, level of noise in the output, interaction between features and increasing number of samples. The results obtained for the four filters studied (ReliefF, Correlation-based Feature Selection, Fast Correlated Based Filter and INTERACT) are compared and discussed. The final aim of this study is to select a filter to construct a hybrid method for feature selection.


Feature Selection Relevant Feature Feature Subset Filter Method Irrelevant Feature 
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

  • Noelia Sánchez-Maroño
    • 1
  • Amparo Alonso-Betanzos
    • 1
  • María Tombilla-Sanromán
    • 1
  1. 1.University of A Coruña, Department of Computer Science, 15071 A CoruñaSpain

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