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Feature Selection for High-Dimensional Data — A Pearson Redundancy Based Filter

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Part of the Advances in Soft Computing book series (AINSC,volume 45)


An algorithm for filtering information based on the Pearson χ2 test approach has been implemented and tested on feature selection. This test is frequently used in biomedical data analysis and should be used only for nominal (discretized) features. This algorithm has only one parameter, statistical confidence level that two distributions are identical. Empirical comparisons with four other state-of-the-art features selection algorithms (FCBF, CorrSF, ReliefF and ConnSF) are very encouraging.


  • Feature Selection
  • Feature Subset
  • Feature Selection Algorithm
  • Redundant Feature
  • Relevance Index

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  • DOI: 10.1007/978-3-540-75175-5_30
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© 2007 Springer-Verlag Berlin Heidelberg

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Biesiada, J., Duch, W. (2007). Feature Selection for High-Dimensional Data — A Pearson Redundancy Based Filter. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75174-8

  • Online ISBN: 978-3-540-75175-5

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