Multi-target regression is concerned with the simultaneous prediction of
multiple continuous target variables based on the same set of input variables.
It arises in several interesting industrial and environmental application
domains, such as ecological modelling and energy forecasting. This paper
presents an ensemble method for multi-target regression that constructs new
target variables via random linear combinations of existing targets. We discuss
the connection of our approach with multi-label classification algorithms, in
particular RA$k$EL, which originally inspired this work, and a family of recent
multi-label classification algorithms that involve output coding. Experimental
results on 12 multi-target datasets show that it performs significantly better
than a strong baseline that learns a single model for each target using
gradient boosting and compares favourably to multi-objective random forest
approach, which is a state-of-the-art approach. The experiments further show
that our approach improves more when stronger unconditional dependencies exist
among the targets.

本文介绍了一种用于多目标回归的集成方法，通过现有目标的随机线性组合构建新的目标变量，并与 RAkEL 和一系列最新的多标签分类算法进行比较。在 12 个多目标数据集上的实验证明，该方法表现显著优于单一模型方法和随机森林方法。

随机线性目标组合多目标回归

Multi-Target Regression via Random Linear Target Combinations

In this paper we study output coding for multi-label prediction. For a
multi-label output coding to be discriminative, it is important that codewords
for different label vectors are significantly different from each other. In the
meantime, unlike in traditional coding theory, codewords in output coding are
to be predicted from the input, so it is also critical to have a predictable
label encoding.
To find output codes that are both discriminative and predictable, we first
propose a max-margin formulation that naturally captures these two properties.
We then convert it to a metric learning formulation, but with an exponentially
large number of constraints as commonly encountered in structured prediction
problems. Without a label structure for tractable inference, we use
overgenerating (i.e., relaxation) techniques combined with the cutting plane
method for optimization.
In our empirical study, the proposed output coding scheme outperforms a
variety of existing multi-label prediction methods for image, text and music
classification.

本文研究了用于多标签预测的输出编码问题，并提出了一种同时具有差异性和可预测性的最大间隔度量学习方法，该方法在图像、文本和音乐分类方面的实证研究中优于其他多标签预测方法。