par Lazar, Cosmin;Taminau, Jonatan;Meganck, Stijn;Steenhoff, David;Nowe, Ann 
;Coletta, Alain ;Molter, Colin ;De Schaetzen, Virginie ;Duque, Robin ;Bersini, Hugues
Référence IEEE/ACM transactions on computational biology and bioinformatics, 9, 4, page (1106-1119), 6202798
Publication Publié, 2012
;Coletta, Alain ;Molter, Colin ;De Schaetzen, Virginie ;Duque, Robin ;Bersini, Hugues Référence IEEE/ACM transactions on computational biology and bioinformatics, 9, 4, page (1106-1119), 6202798
Publication Publié, 2012
Article révisé par les pairs
| Résumé : | A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. We present them in a unified framework, using standardized notations in order to reveal their technical details and to highlight their common characteristics as well as their particularities. © 2012 IEEE. |
