Extracting influence relationships in China’s industrial ecological transformation using a rough set based machine learning method

China’s industry urgently needs to be transformed from the development patterns driven by traditional production factor to achieving industrial ecological transformation (IET). The IET is influenced by diversified factors including resource input, allocation and flow, environmental regulations and technological innovations in different situation. Revealing the complex influence mechanisms between IET and its influence factors is necessary for effectively analyzing, evaluating and improving the performance of IET. A three stages machine learning method including learning, verification and generalization based on dominance-based rough set approach is presented to extract the influential relationships between the IET and its contextual influence factors. The proposed method excavates and learns the historical panel data of China’s 30 provinces, and the cross-validation is conducted to produce a set of highly credible "If-Then" decision rules to generalize the synergistic influential relationships and intensities in IET. The results show that China’s investment strength, resource allocation efficiency, command controlled and economic incentive environmental regulations are determinants to enhance the performance of IET, which helps to select the optimal transformation patterns by taking the historical development characteristics as lessons.