%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Sokolovska, N.
%A Teytaud, O.
%A Rizkalla, S.
%A Consortium, M.
%A Clement, K.
%A Zucker, Jean-Daniel
%T Sparse zero-sum games as stable functional feature selection
%D 2015
%L fdi:010065259
%G ENG
%J Plos One
%@ 1932-6203
%M ISI:000360437700013
%N 9
%P e0134683 [16 ]
%R 10.1371/journal.pone.0134683
%U https://www.documentation.ird.fr/hor/fdi:010065259
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers17-08/010065259.pdf
%V 10
%W Horizon (IRD)
%X 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.
%$ 020