采用负相关学习的SVM集成算法  

SVM Ensembles Algorithm Using Negative Correlation Learning

作　　者：洪铭 汪鸿翔 刘晓芳 柳培忠 HONG Ming;WANG Hongxiang;LIU Xiaofang;LIU Peizhong(College of Engineering,Huaqiao University,Quanzhou 362021,China)

机构地区：[1]华侨大学工学院,福建泉州362021

出　　处：《华侨大学学报（自然科学版）》2018年第6期942-946,共5页Journal of Huaqiao University(Natural Science)

基　　金：国家自然科学基金资助项目(61203242);福建省泉州市科技计划项目(2014Z113;2014Z103);华侨大学研究生科研创新能力培育计划资助项目(1400422003)

摘　　要：为了平衡集成学习中多样性与准确性之间的关系,并提高决策分类器的泛化能力,提出一种基于负相关学习和AdaBoost算法的支持向量机(SVM)集成学习方法.将负相关学习理论融合到AdaBoost-SVM的训练过程中,利用负相关学习理论计算基分类器间的相关性,并根据相关性的值自适应调整基分类器的权重,进而得到加权后的决策分类器.在UCI数据集中进行仿真,结果表明:相较于传统的负相关集成学习算法和AdaBoost-SVM算法,所提出的方法分类准确率更高,泛化能力更好.In order to balance the relationship between diversity and accuracy in ensemble learning,and improve the generalization ability of decision classifier,a new support vector machine(SVM)ensemble learning method based on negative correlation learning and AdaBoost algorithm is proposed.The negative correlation learning theory is integrated into the training process of AdaBoost-SVM,and the correlation between the base classifiers is calculated by using the negative correlation learning theory.Furthermore,the weight of the base classifier is adjusted adaptively according to the correlation value.The simulation results of UCI dataset show that compared with the traditional negative correlation ensemble learning algorithm and AdaBoost-SVM algorithm,the proposed method can get higher classification accuracy and better generalization ability.

关 键 词：负相关学习 误差-分歧分解 AdaBoost-SVM 集成学习 分类器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]