Journal
INFORMATION SCIENCES
Volume 181, Issue 1, Pages 115-128Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.08.047
Keywords
Feature selection; Embedded methods; Support vector machines; Mathematical programming
Categories
Funding
- Chilean Instituto Sistemas Complejos de Ingeniera [ICM: P-05-004-F, CONICYT: FBO16]
- CONICYT
Ask authors/readers for more resources
We introduce an embedded method that simultaneously selects relevant features during classifier construction by penalizing each feature's use in the dual formulation of support vector machines (SVM). This approach called kernel-penalized SVM (KP-SVM) optimizes the shape of an anisotropic RBF Kernel eliminating features that have low relevance for the classifier. Additionally, KP-SVM employs an explicit stopping condition, avoiding the elimination of features that would negatively affect the classifier's performance. We performed experiments on four real-world benchmark problems comparing our approach with well-known feature selection techniques. KP-SVM outperformed the alternative approaches and determined consistently fewer relevant features. (C) 2010 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available