An ensemble-adaptive tree-based chain framework for multi-target regression problems

Multi-target regression has always been a challenging task in engineering applications. Nevertheless, it is easy to encounter problems such as low accuracy and inadequate robustness in some scenarios. To address these issues, an ensemble strategy considering correlations is proposed, named Ensemble-Adaptive Tree-based Correlation Chains. Specifically, a Follow-up Correlation Chaining strategy that quantifies the relationships among targets by arranging the L1 norms of correlations is suggested. Compared with other related strategies, it allows for the representation of these relationships through a single regressor chain. Under the proposed framework, the ensemble strategy integrates ten chains, wherein each chain adaptively updates the sample weights during training. This process involves employing the out-of-sample observations with new convergence criteria. Furthermore, the eXtreme Gradient Boosting is introduced as the base regressor to enhance the overall accuracy of the entire method. Finally, the proposed method is validated based on 25 multi-target datasets and a lightweight design of a high-speed rail bogie. The results demonstrate the superior accuracy and robustness compared to other state-of-the-art methods. In general, this study provides reliable predictions for specific scenarios and delivers practical significance in addressing relevant problems.