Title :
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
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;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Conference_Location :
Edinburgh, Scotland
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1554745
