Sparse Zero-Sum Games as Stable Functional Feature Selection - Sorbonne Université Access content directly
Journal Articles PLoS ONE Year : 2015

Sparse Zero-Sum Games as Stable Functional Feature Selection


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.
Fichier principal
Vignette du fichier
journal.pone.0134683.pdf (1.83 Mo) Télécharger le fichier
Origin : Publication funded by an institution

Dates and versions

hal-01223887 , version 1 (03-11-2015)





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



Gmail Facebook X LinkedIn More