Multi-target regression (MTR) refers to learning multiple relevant regression tasks simultaneously. The main challenges of multi-target regression arise from modeling the underlying relationships between input features and output target variables as well as exploring inter-target correlations. In this paper

we propose a multi-target regression method via sparse integration and label-specific features (SI-LSF) that utilizes inter-target correlations to improve the overall prediction accuracy by constructing label-specific features and deals with the input-output relationships through sparse integration of various regression models. Extensive experimental evaluation on 18 benchmark datasets demonstrates that our proposed method can achieve competitive performance against representative state-of-the-art multi-target regression methods

which shows the great effectiveness in dealing with multivariate prediction.