An Improved Adaptive Support Vector Machine Algorithm with Combinational Fuzzy C-means Clustering

摘要：

In order to improve the training efficiency to the data set, an improved adaptive Support Vector Machine (SVM) algorithm with combinational Fuzzy C-means Clustering is proposed. With multi-layer fuzzy C-means clustering algorithm original data are pretreated to remove the training data, which has no contribution to the classification. The remaining data are used to complete the training work for SVM to obtain the optimal hyper-plane. Besides, the parameter adaptive optimization algorithm has both increased the flexibility of parameter selection for SVM and enhanced the convergence speed. In the end, derived from the comparison of testing performance using the data set from the database of Statlog, the experiment result indicates that the proposed algorithm can both shorten the training time and provides high accuracy and excellent generalization, also it can keep the distribution of original data set at the same time.

关键词： Support Vector Machine Fuzzy C-means Clustering Statlog

作者: Jun Li Zhiyu Yu

作者单位: College of Computer Science, Sichuan University Chengdu 610065, China College of communication and e School of Electrical Engineering Southwest Jiaotong University Chengdu, Sichuan 610031, China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 269-272

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）