Genetic Fuzzy Fusion of SVM Classifiers for Biomedical Data
Xiujuan Chen, Robert W. Harrison, Yanqing Zhang · 2005
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.