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Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selection

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Abstract

In this paper, we propose an efficient and robust approach for semi-supervised feature selection, based on the constrained Laplacian score. The main drawback of this method is the choice of the scant supervision information, represented by pairwise constraints. In fact, constraints are proven to have some noise which may deteriorate learning performance. In this work, we try to override any negative effects of constraint set by the variation of their sources. This is achieved by an ensemble technique using both a resampling of data (bagging) and a random subspace strategy. Experiments on high-dimensional datasets are provided for validating the proposed approach and comparing it with other representative feature selection methods.

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Notes

  1. http://perso.univ-lyon1.fr/haytham.elghazel/EnsCLS/EnsCLS.zip.

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Acknowledgments

We thank anonymous reviewers for their very useful comments and suggestions.

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Authors and Affiliations

  1. University of Lyon1 - LIRIS, 43 Bd du 11 Novembre 1918, 69622, Villeurbanne, France

    Khalid Benabdeslem & Haytham Elghazel

  2. Computer Science Department, Zirve University, Kizilhisar Campus, 27260, Gaziantep, Turkey

    Mohammed Hindawi

Authors
  1. Khalid Benabdeslem
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  2. Haytham Elghazel
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  3. Mohammed Hindawi
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Corresponding author

Correspondence to Khalid Benabdeslem.

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The authors declare that they have no conflict of interest.

Additional information

This work represents an extension of our recently presented idea on the workshop Copem@ECML/PKDD’13 [23].

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Benabdeslem, K., Elghazel, H. & Hindawi, M. Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selection. Knowl Inf Syst 49, 1161–1185 (2016). https://doi.org/10.1007/s10115-015-0901-0

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