Title :
Feature weighting based on L-GEM
Author :
Wang, Qian-cheng ; Ng, Wing W Y ; Chan, Patrick P K ; Yeung, Daniel S.
Author_Institution :
Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China
Abstract :
In this paper, we propose a novel method to weight features for their relevance to the given classification problem. The weight of a feature is computed by its Localized Generalization Error model (L-GEM). Then, a Radial Basis Function Neural Network (RBFNN) is trained by those weighted features. Experimental results on image classification problem show that the proposed method is efficient and effective in comparison to current methods.
Keywords :
image classification; radial basis function networks; L-GEM; RBFNN; classification problem; feature weighting method; image classification problem; localized generalization error model; radial basis function neural network; Cybernetics; Image classification; Image color analysis; Machine learning; Neurons; Training; Transform coding; Feature weighting; Image classification; Localized Generalization Error Model; RBFNN;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5581062
