文献类型：会议论文

英文题名：Applied research of the algorithm combined of PCA and SVMS on stock features

作者：Chun, Cai[1]; Liu, Yuanhong[1]; Sun, Jianhua[1]

第一作者：蔡春

通讯作者：Chun, C.

机构：[1] Arts and Science College, Beijing Union University, Beijing, China

第一机构：北京联合大学应用文理学院

会议论文集：ICCASM 2010 - 2010 International Conference on Computer Application and System Modeling, Proceedings

会议日期：22 October 2010 through 24 October 2010

会议地点：Shanxi, Taiyuan

语种：英文

外文关键词：Data mining - Support vector machines

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. ? 2010 IEEE.