Boosting Decision Trees for Prediction in E-Commerce

摘要：

One of the major challenging tasks in E-Commerce is the prediction and classification of commercial data. Boosting is a popular ensemble learning method and can improve results of classification algorithms. In this paper, a boosting decision trees algorithm is proposed to analyze and predict the commercial data. An experimental evaluation is carried out on public commercial dataset and the experimental results show that the proposed method improves performance of prediction obviously.

关键词： boosting decision trees E-Commerce

作者: Lei Shi Mei Weng 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: 2010 International Conference on Information Technology and Industrial Engineering(2010年信息技术与工业工程国际学术会议 ITIE 2010)

会议地点: 武汉

会议语种:英文

页码: 281-284

在线出版日期: 2010-06-06（万方平台首次上网日期，不代表论文的发表时间）