Algorithm of selective SVM ensemble
Tao Chen · Jisuanji gongcheng yu sheji · 2011
Selective SVM ensemble based on differential evolution and negative correlation learning is presented to improve the generalization ability of SVM.Many SVMs are produced by bootstrap methods,the fitness function is established based on negative correlation learning to improve generalization and high dissimilarity with others.The weighte of SVM is calculated by differential evolution,then those SVMs with weight larger than a given threshold value are ensembled using weights average.Experimental results show that the algorithm is an effect ensemble method and improves the generalization ability of SVM.