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Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features

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Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12565))

Abstract

For data sets with similar features, for example highly correlated features, most existing stability measures behave in an undesired way: They consider features that are almost identical but have different identifiers as different features. Existing adjusted stability measures, that is, stability measures that take into account the similarities between features, have major theoretical drawbacks. We introduce new adjusted stability measures that overcome these drawbacks. We compare them to each other and to existing stability measures based on both artificial and real sets of selected features. Based on the results, we suggest using one new stability measure that considers highly similar features as exchangeable.

The source code for the experiments and analyses of this article is publicly available at https://github.com/bommert/adjusted-stability-measures.

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Acknowledgements

This work was supported by German Research Foundation (DFG), Project RA 870/7-1 and Collaborative Research Center SFB 876, A3. We acknowledge the computing time provided on the Linux HPC cluster at TU Dortmund University (LiDO3), partially funded in the course of the Large-Scale Equipment Initiative by the German Research Foundation (DFG) as Project 271512359.

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Authors and Affiliations

  1. TU Dortmund University, 44221, Dortmund, Germany

    Andrea Bommert & Jörg Rahnenführer

Authors
  1. Andrea Bommert
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  2. Jörg Rahnenführer
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Corresponding author

Correspondence to Andrea Bommert .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Bommert, A., Rahnenführer, J. (2020). Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_19

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  • DOI: https://doi.org/10.1007/978-3-030-64583-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64582-3

  • Online ISBN: 978-3-030-64583-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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