title: Feature selection for linear SVMs under uncertain data: robust optimization based on difference of convex functions algorithms.
abstract: In this paper, we consider the problem of feature selection for linear SVMs on uncertain data that is inherently prevalent in almost all datasets. Using principles of Robust Optimization, we propose robust schemes to handle data with ellipsoidal model and box model of uncertainty. The difficulty in treating ℓ0-norm in feature selection problem is overcome by using appropriate approximations and Difference of Convex functions (DC) programming and DC Algorithms (DCA). The computational results show that the proposed robust optimization approaches are superior than a traditional approach in immunizing perturbation of the data.
authors: Laboratory of Theoretical and Applied Computer Science EA 3097 University of Lorraine, Ile du Saulcy, 57045 Metz, France; Lorraine Research Laboratory in Computer Science and its Applications CNRS UMR 7503, University of Lorraine, 54506 Nancy, France. Electronic address: hoai-an.le-thi@univ-lorraine.fr.
authors: Hoai An
authors: HA
authors: Le Thi
authors: Laboratory of Theoretical and Applied Computer Science EA 3097 University of Lorraine, Ile du Saulcy, 57045 Metz, France. Electronic address: xuan-thanh.vo5@etu.univ-lorraine.fr.
authors: Xuan Thanh
authors: XT
authors: Vo
authors: Laboratory of Mathematics, INSA - Rouen, University of Normandie, 76801 Saint-Etienne-du-Rouvray Cedex, France. Electronic address: pham@insa-rouen.fr.
authors: Tao
authors: T
authors: Pham Dinh
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D007938:Leukemia / Q000235:genetics; D016014:Linear Models; D046228:Microarray Analysis; D060388:Support Vector Machine; D014887:Weather
keywords: DC programming; DCA; Feature selection; Robust optimization; SVM