Customers Mining of Logistics Industry Based on Neuro-Fuzzy Decision Tree
Hongxia Jin, Xiaoye Niu, Zhang Li, Dongyan Zhang · 2007
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 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.