Genetic fuzzy fusion of SVM classifiers for biomedical data

Author

Chen, Ziujuan ; Harrison, Robert ; Zhang, Yan-Qing

Author_Institution

Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA

Volume

1

fYear

2005

fDate

5-5 Sept. 2005

Firstpage

654

Abstract

Combining multiple classifiers is a natural way to discover useful information and improve the performance of individual classifiers. In this paper, we propose one approach to combine multiple SVMs and improve the generalization ability of SVM classifiers. One fuzzy system is constructed based on SVM accuracies and distances of data examples to SVM hyperplanes. The output fuzzy membership functions of the fuzzy system are tuned by a genetic algorithm (GA). The established model is applied on an ovarian cancer dataset and the experiment shows the proposed genetic fuzzy model performs more stable and more reliable than individual SVMs

Keywords

cancer; fuzzy set theory; fuzzy systems; generalisation (artificial intelligence); genetic algorithms; medical computing; pattern classification; support vector machines; SVM hyperplanes; biomedical data; fuzzy system; generalization ability; genetic algorithm; genetic fuzzy fusion model; multiple SVM classifiers; ovarian cancer dataset; Bagging; Bioinformatics; Biological system modeling; Computer science; Fuzzy systems; Genetic algorithms; Kernel; Support vector machine classification; Support vector machines; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2005. The 2005 IEEE Congress on

Conference_Location

Edinburgh, Scotland

Print_ISBN

0-7803-9363-5

Type

conf

DOI

10.1109/CEC.2005.1554745

Filename

1554745