Research in imbalanced domain learning has almost exclusively focused on solving classification tasks for accurate prediction of cases labelled with a rare class. Approaches for addressing such problems in regression tasks are still scarce due to two main factors. First, standard regression tasks assume each domain value as equally important. Second, standard evaluation metrics focus on assessing the performance of models on the most common values of data distributions. In this paper, we present an approach to tackle imbalanced regression tasks where the objective is to predict extreme (rare) values. We propose an approach to formalise such tasks and to optimise/evaluate predictive models, overcoming the factors mentioned and issues in related work. We present an automatic and non-parametric method to obtain relevance functions, building on the concept of relevance as the mapping of target values into non-uniform domain preferences. Then, we propose SERA, a new evaluation metric capable of assessing the effectiveness and of optimising models towards the prediction of extreme values while penalising severe model bias. An experimental study demonstrates how SERA provides valid and useful insights into the performance of models in imbalanced regression tasks.

中文翻译：

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不平衡回归和极值预测

不平衡域学习的研究几乎完全专注于解决分类任务，以准确预测标有稀有类别的案例。由于两个主要因素，在回归任务中解决此类问题的方法仍然很少。首先，标准回归任务假设每个域值同等重要。其次，标准评估指标侧重于评估模型在最常见的数据分布值上的性能。在本文中，我们提出了一种解决不平衡回归任务的方法，其目标是预测极端（罕见）值。我们提出了一种方法来正式化这些任务并优化/评估预测模型，克服相关工作中提到的因素和问题。我们提出了一种自动和非参数的方法来获得相关函数，建立在相关性概念的基础上，将目标值映射到非统一的领域偏好。然后，我们提出了 SERA，这是一种新的评估指标，能够评估有效性并优化模型以预测极值，同时惩罚严重的模型偏差。一项实验研究展示了 SERA 如何为不平衡回归任务中模型的性能提供有效且有用的见解。