A Hybrid Machine Learning Approach in Predicting E-Commerce Supply Chain Risks

Abstract

The objective of this paper is to propose a hybrid machine learning approach using a so-called Cost-Complexity Pruning Decision Trees algorithm in predicting supply chain risks, particularly, delayed deliveries. The Recursive Feature Elimination with Cross-Validation solution is designed to improve the feature selection function of the Decision Trees classifier. Then, the Two-Phase Cost-Complexity Pruning technique is developed to reduce the overfitting of the tree-based algorithms. A case study of an e-commerce enabler in Vietnam is investigated to illustrate the efficiency of the proposed models. The obtained results show promise in terms of predictive performance.