Support Vector Machine Ensemble Based on Independent Component Analysis and Fuzzy Kernel Clustering

摘要：

In order to improve the generalization performance of support vector machine (SVM), a support vector machine ensembling method based on independent component analysis (ICA) and fuzzy kernel clustering (FKC) was proposed. The ICA emphasizes the independence between the data characteristics and can effectively obtain a series of independent features, the performance of single SVM can be improved when the SVM was trained on these independent features; The FKC method uses kernel function to expand the feature space, so that the clustering is more accurate and the diversity between sub-SVMs can also be guaranteed. Simulation results on UCI datasets show that the proposed ensembling method can improve the classification precision of SVM and make the ensemble SVM has better generalization property.

关键词： Support vector machine Ensemble learning Independent component analysis Fuzzy kernel clustering

作者: Yong Ma Xiaoxiao Kong Xuesong Wang

作者单位: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou,

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 752-755

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）