Many real-world data mining applications involve obtaining predictive models using imbalanced datasets. Frequently, the least common target variables present within datasets are associated with events that are highly relevant for end users. When these variables are nominal, we have a class-imbalance problem which has been thoroughly studied within machine learning. As for regression tasks where target variables are continuous, few predictive models and evaluation techniques exist. This paper proposes a solution to these challenges. First, we introduce a cost-sensitive learning algorithm based on a neural network trained on the minimization of a biased loss function. Results show a higher or comparable performance and convergence speed to existent techniques. Second, we develop new approaches for performance assessment of regression tasks within imbalanced domains by proposing new scalar measures, namely Geometric Mean Error (GME) and Class-Weighted Error (CWE), as well as new graphical-based measures, namely REC_TPR, REC_TNR, REC_{G − Mean} and REC_CWA curves. Unlike standard measures, our evaluation strategies are shown to be more robust to data imbalance as they reflect the performance of both rare and frequent events.

中文翻译：

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新型成本敏感算法和不平衡域回归的新评估策略

许多现实世界的数据挖掘应用程序涉及使用不平衡数据集获取预测模型。通常，数据集中存在的最不常见的目标变量与与最终用户高度相关的事件相关。当这些变量是名义值时，我们就会遇到一个类不平衡问题，该问题已在机器学习中进行了深入研究。对于目标变量连续的回归任务，几乎没有预测模型和评估技术。本文提出了应对这些挑战的解决方案。首先，我们介绍一种基于神经网络的成本敏感型学习算法，该神经网络经过最小化有偏损失函数训练。结果表明，与现有技术相比，其性能和收敛速度更高或相当。第二，GME）和类的加权误差（ CWE），以及新的基于图形的措施，即REC _TPR， REC _TNR， REC _{ģ  - 平均数}和REC _CWA曲线。与标准措施不同，我们的评估策略被证明对数据不平衡更为稳健，因为它们反映了罕见事件和频繁事件的表现。