Multi-target regression (MTR) aimed to predict multiple continuous variables using a common set of input variables

whose existing methods could be classified as problem transformation methods (PTM) and algorithm adaptation methods (AAM). Its main challenges were how to model the complex relationship between input and output space

and how to effectively utilize the correlation between targets. However

the existing PTM rarely took both aspects into consideration. So

this paper constructs a problem transformation method named TSF-TC combining target-specific features and Inter-Target correlations. TSF-TC constructs specific features to per target by conducting clustering analysis on the samples after binning

and then reveals the correlation between targets by selectively stacking single target prediction. Comparative experiments with existing multi-target regression methods on 18 datasets fully demonstrate the advantages of TSF-TC.