Multi-target regression (MTR) refers to learning multiple relevant regression tasks simultaneously. Although much progress has been made in multi-target regression, there are still two challenging issues, that is, how to model the underlying relationships between input features and output targets, and how to explore inter-target dependencies. In this study, an effective algorithm named LLIC is proposed; it learns local instance correlations to reveal the relationships between features and output targets, and inter-target dependencies. First, an eminent instance selection method is adapted to directly work with multi-target data, constructing a collection of local instances for each instance. Then, in order to exploit the relationships between input features and output targets, and reveal inter-target dependencies, the collection of local instances is divided into two spaces, that is, a feature space and a target space. Implicit features of input features and targets are obtained in a statistical way. Finally, a final prediction model for each output target is trained on an expanded input space where the implicit features are treated as additional input variables. Extensive experiments on 18 benchmark datasets demonstrate that our proposed LLIC method can achieve competitive performance against representative state-of-the-art multi-target regression methods.

多目标回归（MTR）是指同时学习多个相关的回归任务。尽管多目标回归已取得很大进展，但是仍然存在两个具有挑战性的问题，即如何对输入要素和输出目标之间的基础关系进行建模，以及如何探索目标间的依存关系。在这项研究中，提出了一种有效的算法，称为LLIC。它学习局部实例相关性，以揭示要素与输出目标之间的关系以及目标间的依存关系。首先，一种卓越的实例选择方法适用于直接处理多目标数据，为每个实例构建本地实例的集合。然后，为了利用输入要素和输出目标之间的关系，并揭示目标间的依赖性，本地实例的集合分为两个空间，即特征空间和目标空间。输入特征和目标的隐式特征以统计方式获得。最后，在扩展的输入空间上训练每个输出目标的最终预测模型，其中隐式特征被视为其他输入变量。在18个基准数据集上进行的大量实验表明，我们提出的LLIC方法与代表性的最新多目标回归方法相比，具有竞争优势。在扩展的输入空间上训练每个输出目标的最终预测模型，其中隐式特征被视为其他输入变量。在18个基准数据集上进行的大量实验表明，我们提出的LLIC方法与代表性的最新多目标回归方法相比，具有竞争优势。在扩展的输入空间上训练每个输出目标的最终预测模型，其中隐式特征被视为其他输入变量。在18个基准数据集上进行的大量实验表明，我们提出的LLIC方法与代表性的最新多目标回归方法相比，具有竞争优势。