A study on the effect of scaling functions to feature weighting performance

Author

Ng, Wing W Y ; Wang, Qian-cheng ; Yang, Rui-jie ; Chan, Patrick P K ; Yeung, Daniel S.

Author_Institution

Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China

Volume

3

fYear

2011

fDate

10-13 July 2011

Firstpage

1077

Lastpage

1081

Abstract

In this paper, we perform a study on several data scaling functions for feature weighting. In our former study, we have proposed a feature weighting method based on the Localized Generalization Error Model (L-GEM). The function of weighting those inputs is influential to the performance of resulting classifiers. However, there are few researches focusing on how to use feature weights in a better way. In this paper, we study data scaling function for automatic image annotation with Radial Basis Function Neural Network (RBFNN). Experimental results show that a good data scaling functions yields a better image annotation performance for the same set of feature weights.

Keywords

image processing; learning (artificial intelligence); radial basis function networks; L-GEM; Localized Generalization Error Model; automatic image annotation; data scaling functions; feature weighting performance; radial basis function neural network; Accuracy; Classification algorithms; Cybernetics; Equations; Machine learning; Neurons; Testing; Automatic image annotation; Data scaling function; Feature weighting; Localized Generalization Error Model; RBFNN;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016930

Filename

6016930