Feature analysis: support vector machine approaches
Yan Shi, Tianxu Zhang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
This paper demonstrates a novel criterion for both feature ranking and feature selection using Support Vector Machines (SVMs). The method analyses the importance of feature subset using the bound on the expected error probability of an SVM. In addition a scheme for feature ranking based on SVMs is presented. Experiments show that the proposed schemes perform well in feature ranking/selection, and risk bound based criterion is superior to some other criterions.