关键词: 集成学习, 支持向量机, 自动模型选择, 多模式扰动, 主成分分析

Abstract: According to the fact that the bootstrap in ensemble learning can’t generate the committee classifiers with big differences，automatical weighted SVM ensemble learning based on multimodal perturbation is proposed（MP-AWE）.MP-AWE generates the perturbation on the training data with bootstrap sampling，generates the perturbation on the input attributes with PCA attribute subspace selection，and generates the perturbation on the learning parameter with automatic model selection，and generates the perturbation on the output with accuracy of the committee classifiers.The experimental results show that the performance of MP-AWE is better than that of many other ensemble algorithms.

Key words: ensemble learning, Support Vector Machine（SVM）, automatic model selection, multimodal perturbation, Principal Component Analysis（PCA）