Applied Research of the Algorithm Combined of PCA and SVMS on Stock Features

摘要：

For the problem of feature selection of stock, this paper presents a new algorithm which is the optimal combination of Principle Components Analysis (PCA) with Support Vector Machines (SVMs).The new algorithm is based on weight measure. Because of specialty of this problem, a weight measure is learned by PCA and SVMs with linear kernel function. Good stock and bad stock with many features belong to two classifications. Experiments prove the effective of our method compared with traditional feature selection.

关键词： support vector machines principle components analysis feature selection data mining

作者: Cai Chun Yuanhong Liu Jianhua Sun

作者单位: Arts & Science College Beijing Union University Beijing, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 432-434

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）