Support vector machine ensemble model based on KFCM and its application
To further enhance the regression prediction accuracy of support vector machine,a Least Squares Support Vector Machine (LS-SVM) ensemble model based on Kernel Fuzzy C-Means clustering (KFCM) was proposed. The KFCM algorithm was used to classify LS-SVMs trained independently by its output on validate samples,the generalization errors of LS-SVMs in each category to the validate set were calculated of the LS-SVM whose error was minimum would be selected as the representative of its category,and then the final prediction was obtained by simple average of the predictions of the component LS-SVM. The experiments in short-term load forecasting show the proposed approach has higher accuracy.
Least Squares Support Vector Machine (LS-SVM)Kernel Fuzzy C-Means clustering (KFCM)ensemble learningshort-term load forecasting
NETL
NSTL
万方数据
维普
