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The Influence of Multi-class Feature Selection on the Prediction of Diagnostic Phenotypes

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Abstract

In this work, we evaluate two schemes for incorporating feature selection processes in multi-class classifier systems on high-dimensional data of low cardinality. These schemes operate on the level of the systems’ individual base classifiers and therefore do not perfectly fit in the traditional categories of filter, wrapper and embedded feature selection strategies. They can be seen as two examples of feature selection networks that are only loosely related to the structure of the multi-class classifier system. The architectures are tested for their application in predicting diagnostic phenotypes from gene expression profiles. Their selection stability and the overall generalization ability are evaluated in \(10 \times 10\) cross-validation experiments with support vector machines, random forests and nearest neighbor classifiers on eight publicly available multi-class microarray datasets. Overall the feature selecting multi-class classifier systems were able to outperform their counterparts on at least five of eight datasets.

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Acknowledgements

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 602783, the German Research Foundation (DFG, SFB 1074 project Z1), and the Federal Ministry of Education and Research (BMBF, Gerontosys II, Forschungskern SyStaR, ID 0315894A and e:Med, SYMBOL-HF, ID 01ZX1407A) all to HAK.

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Author notes

  1. L. Lausser and R. Szekely have contributed equally to this work.

Authors and Affiliations

  1. Institute of Medical Systems Biology, Ulm University, 89069, Ulm, Germany

    Ludwig Lausser, Robin Szekely, Lyn-Rouven Schirra & Hans A. Kestler

  2. Institute of Number Theory and Probability Theory, Ulm University, 89069, Ulm, Germany

    Lyn-Rouven Schirra

  3. Leibniz Institute on Aging – Fritz Lipmann Institute, 07745, Jena, Germany

    Hans A. Kestler

Authors
  1. Ludwig Lausser
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  2. Robin Szekely
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  4. Hans A. Kestler
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Corresponding author

Correspondence to Hans A. Kestler.

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Lausser, L., Szekely, R., Schirra, LR. et al. The Influence of Multi-class Feature Selection on the Prediction of Diagnostic Phenotypes. Neural Process Lett 48, 863–880 (2018). https://doi.org/10.1007/s11063-017-9706-3

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