K-mean and Double Cross-Validation Algorithm for LS-SVM in Sasang Typology Classification

摘要：

The Sasang typology is the traditional typology theory in Oriental Medicine.The medical typology distributes people into four types based on their traits:Tae-Yang,So-Yang, Tae-Eum and So-Eum type.In the paper,we design a model for Sasang typology classification based on the least squares support vector machines (LS-SVM).We use k-mean algorithm for decision the feature index from the side face,then we add the side face ratios into our feature space for improvement.The choice of resample method is very important for parametersoptimization in SVM and it will influence the systems stability.We propose a novel resampling algorithm called double cross-validation through comparing the cross-validation with bootstrap approach. The result shows that the established model under double cross- validation algorithm based on LS-SVM is more robust with high performance and suffices for the requirements of control and optimization for classification processes.

关键词： Double Cross-Validation Least Squares Support Vector Machines Bootstrap Sasang Typology Classification K- mean algorithm

作者: Qian Zhang Ki Jung Lee Taeg Keun Whangbo

作者单位: Department of Computer Science Kyungwon University Sujung-Gu,Songnam,Kyunggi-Do,Korea

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）