Application Study of Corporate Profit Prediction Using Decision Tree Ensemble Model

摘要：

In this paper,we apply ensemble method to predict the future profit states of the listed enterprises.We take decision tree model as the basic classifier to construct the ensemble model by means of Bagging technique.The empirical result shows that the rate of prediction accuracy of the ensemble model is greater than 96%.We argue that the ensemble model can improve the prediction accuracy and is more robust to the individual decision tree model.By comparing the results derived from different ensemble models which are constructed by different number of base classifiers,we find that the ensemble model constructed by 35 base classifiers works best.

关键词： decision tree ensemble learning forecasting classification EPS

作者: Qiujun Lan Xiaobin Yang Chaoqun Ma

作者单位: School of Business Administration,Hunan University,Changsha,Hunan 410082,China

会议类型: 国际会议

会议名称: The 2008 International Conference on Business Intelligence and Financial Engineering(BIFE 2008)(商业智能和金融工程国际会议)

会议地点: 长沙

会议语种:英文

页码: 787-791

在线出版日期: 2008-10-28（万方平台首次上网日期，不代表论文的发表时间）