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Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets

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  • Lagani, Vincenzo
  • Athineou, Giorgos
  • Farcomeni, Alessio
  • Tsagris, Michail
  • Tsamardinos, Ioannis

Abstract

The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. Under that respect SES subsumes and extends previous feature selection algorithms, like the maxmin parent children algorithm. SES is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning 'mind from the machine' in Latin. The MXM implementation of SES handles several data-analysis tasks, namely classi�cation, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of predictive accuracy and that multiple, equally predictive signatures are actually present in real world data.

Suggested Citation

  • Lagani, Vincenzo & Athineou, Giorgos & Farcomeni, Alessio & Tsagris, Michail & Tsamardinos, Ioannis, 2016. "Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets," MPRA Paper 72772, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:72772
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    References listed on IDEAS

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    4. Müssel, Christoph & Lausser, Ludwig & Maucher, Markus & Kestler, Hans A., 2012. "Multi-Objective Parameter Selection for Classifiers," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(i05).
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    6. Calcagno, Vincent & de Mazancourt, Claire, 2010. "glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i12).
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    Cited by:

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    2. Sašo Karakatič, 2020. "EvoPreprocess—Data Preprocessing Framework with Nature-Inspired Optimization Algorithms," Mathematics, MDPI, vol. 8(6), pages 1-29, June.

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    More about this item

    Keywords

    feature selection; constraint-based algorithms; multiple predictive signatures;
    All these keywords.

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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