Multi-output learning aims to simultaneously predict multiple outputs given an input. It is an important learning problem due to the pressing need for sophisticated decision making in real-world applications. Inspired by big data, the 4Vs characteristics of multi-output imposes a set of challenges to multi-output learning, in terms of the volume, velocity, variety and veracity of the outputs. Increasing number of works in the literature have been devoted to the study of multi-output learning and the development of novel approaches for addressing the challenges encountered. However, it lacks a comprehensive overview on different types of challenges of multi-output learning brought by the characteristics of the multiple outputs and the techniques proposed to overcome the challenges. This paper thus attempts to fill in this gap to provide a comprehensive review on this area. We first introduce different stages of the life cycle of the output labels. Then we present the paradigm on multi-output learning, including its myriads of output structures, definitions of its different sub-problems, model evaluation metrics and popular data repositories used in the study. Subsequently, we review a number of state-of-the-art multi-output learning methods, which are categorized based on the challenges.

中文翻译：

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多输出学习调查

多输出学习旨在在给定输入的情况下同时预测多个输出。由于在实际应用中迫切需要进行复杂的决策，因此这是一个重要的学习问题。受大数据的启发，多输出的 4V 特性对多输出学习提出了一系列挑战，包括输出的数量、速度、多样性和准确性。越来越多的文献致力于研究多输出学习和开发应对所遇到挑战的新方法。然而，它缺乏对多输出学习的不同类型挑战的全面概述，以及多输出的特性和提出的克服挑战的技术。因此，本文试图填补这一空白，以对该领域进行全面审查。我们首先介绍输出标签生命周期的不同阶段。然后我们介绍了多输出学习的范式，包括其无数的输出结构、其不同子问题的定义、模型评估指标和研究中使用的流行数据存储库。随后，我们回顾了一些最先进的多输出学习方法，这些方法根据挑战进行分类。