Customers Mining of Logistics Industry Based on Neuro-Fuzzy Decision Tree

摘要：

Fuzzy decision tree (FDT)is a powerful,top-down, hierarchical search methodology to extract human interpretable classification rules.However,it is poor in classification accuracy. In this paper,neural networks-fuzzy decision tree (Neuro-FDT) is constructed using the method of Rajen B.Bhatt and Gopal:a fuzzy decision tree structure with neural like parameter adaptation strategy.The method improves FDT s classification accuracy and extracts more accuracy human interpretable classification rules.The fuzzy rules enable a decision-maker to adjust corresponding strategy according to different customers. The decision-maker may give some special policies to profit customers.The results of the research indicate that the Neuro-fuzzy decision tree technique is very valid in Logistics industry and it will have a good application prospect in this field. Higher- profit customers.The results of the research indicate that the Neuro-fuzzy decision tree technique is very valid in Logistics industry and it will have a good application prospect in this field.

关键词： Logistics industry Fuzzy Decision Tree Customers Mining Neuro-Fuzzy Decision Tree

作者: Hongxia Jin Xiaoye Niu Li Zhang Dongyan Zhang

作者单位: College of Business Agricultural University of Hebei Baoding 071001,China

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）