| Autor: |
Nataliya Sokolovska, Olivier Teytaud, Salwa Rizkalla, MicroObese consortium, Karine Clément, Jean-Daniel Zucker |
| Jazyk: |
angličtina |
| Rok vydání: |
2015 |
| Předmět: |
|
| Zdroj: |
PLoS ONE, Vol 10, Iss 9, p e0134683 (2015) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0134683 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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