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Sparse Zero-Sum Games as Stable Functional Feature Selection

Author

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  • Nataliya Sokolovska
  • Olivier Teytaud
  • Salwa Rizkalla
  • MicroObese consortium
  • Karine Clément
  • Jean-Daniel Zucker

Abstract

In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints.

Suggested Citation

  • Nataliya Sokolovska & Olivier Teytaud & Salwa Rizkalla & MicroObese consortium & Karine Clément & Jean-Daniel Zucker, 2015. "Sparse Zero-Sum Games as Stable Functional Feature Selection," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-16, September.
    • Handle: RePEc:plo:pone00:0134683
    DOI: 10.1371/journal.pone.0134683
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    References listed on IDEAS

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