Skip to Main content Skip to Navigation
Journal articles

Sparse Zero-Sum Games as Stable Functional Feature Selection

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.
Document type :
Journal articles
Complete list of metadata

Cited literature [38 references]  Display  Hide  Download
Contributor : Gestionnaire Hal-Upmc <>
Submitted on : Tuesday, November 3, 2015 - 4:02:23 PM
Last modification on : Thursday, July 8, 2021 - 3:49:24 AM
Long-term archiving on: : Thursday, February 4, 2016 - 11:16:49 AM


Publication funded by an institution


Distributed under a Creative Commons Attribution 4.0 International License



Nataliya Sokolovska, Olivier Teytaud, Salwa Rizkalla, Karine Clément, Jean-Daniel Zucker. Sparse Zero-Sum Games as Stable Functional Feature Selection. PLoS ONE, Public Library of Science, 2015, 10 (9), pp.e0134683. ⟨10.1371/journal.pone.0134683⟩. ⟨hal-01223887⟩



Record views


Files downloads