Sparse Zero-Sum Games as Stable Functional Feature Selection

Nataliya Sokolovska, Olivier Teytaud, Salwa Rizkalla, MicroObese Consortium, Karine Clément and Jean-Daniel Zucker

PLOS ONE, 2015, vol. 10, issue 9, 1-16

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0134683

DOI: 10.1371/journal.pone.0134683

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