A DC programming approach for feature selection in support vector machines learning |
| |
Authors: | Hoai An Le Thi Hoai Minh Le Van Vinh Nguyen Tao Pham Dinh |
| |
Institution: | 1. Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), UFR MIM, University of Paul Verlaine, Metz, Ile du Saulcy, 57045, Metz, France 2. Laboratory of Modelling, Optimization and Operations Research, National Institute for Applied Sciences, Rouen, BP 08, Place Emile Blondel, 76131, Mont Saint Aignan Cedex, France
|
| |
Abstract: | Feature selection consists of choosing a subset of available features that capture the relevant properties of the data. In supervised pattern classification, a good choice of features is fundamental for building compact and accurate classifiers. In this paper, we develop an efficient feature selection method using the zero-norm l 0 in the context of support vector machines (SVMs). Discontinuity at the origin for l 0 makes the solution of the corresponding optimization problem difficult to solve. To overcome this drawback, we use a robust DC (difference of convex functions) programming approach which is a general framework for non-convex continuous optimisation. We consider an appropriate continuous approximation to l 0 such that the resulting problem can be formulated as a DC program. Our DC algorithm (DCA) has a finite convergence and requires solving one linear program at each iteration. Computational experiments on standard datasets including challenging feature-selection problems of the NIPS 2003 feature selection challenge and gene selection for cancer classification show that the proposed method is promising: while it suppresses up to more than 99% of the features, it can provide a good classification. Moreover, the comparative results illustrate the superiority of the proposed approach over standard methods such as classical SVMs and feature selection concave. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|