Combination with Machine Learning Algorithms for the Classification in E-bussiness

摘要：

　　E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases,browsing pattern and preferences has been generated.Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness.Support Vector Machines are popular and powerful machine learning techniques,and they offer state-of-the-art performance.Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection.In this paper,rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

关键词： Machine learning E-bussiness Rough set

作者: Lei Shi Xinming Ma Xiaohong Hu

作者单位: College of Information and Management Science, HeNan Agricultural University, Zhengzhou 450002, China

会议类型: 国际会议

会议名称: the 2011 International Conference on Frontiers of Manufacturing Science and Measuring Technology (第一届制造科学与检测技术国际会议(ICFMM2011))

会议地点: 重庆

会议语种:英文

页码: 625-628

在线出版日期: 2011-06-23（万方平台首次上网日期，不代表论文的发表时间）