Class imbalance treatment methods and cost-sensitive classification algorithms are typically treated as two independent research areas. However, many of these techniques have properties in common. After providing a background to the two fields of research, this article identifies the fundamental mechanism which is common to both. Using this mechanism, a taxonomy is created which encompasses approaches to both class imbalance treatment and cost-sensitive classification. Through this survey, we aim to bridge the gap between the two fields such that lessons from one field may be applied to the other. Many data mining tasks are naturally both class imbalanced and cost-sensitive. This survey is useful for researchers and practitioners approaching these tasks as it provides a detailed overview of approaches in both fields. Many of the surveyed techniques are classifier independent. However, we chose to focus on techniques which were either decision tree-based or compatible with decision trees. This choice was based on the popularity and novelty of their application to both fields.

中文翻译：

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类不平衡和成本敏感的决策树

类不平衡处理方法和成本敏感分类算法通常被视为两个独立的研究领域。然而，许多这些技术具有共同的特性。在介绍了这两个研究领域的背景之后，本文确定了两者共有的基本机制。使用这种机制，创建了一个分类法，其中包括类不平衡处理和成本敏感分类的方法。通过这项调查，我们旨在弥合两个领域之间的差距，以便将一个领域的经验应用于另一个领域。许多数据挖掘任务自然是类别不平衡和成本敏感的。该调查对处理这些任务的研究人员和从业者很有用，因为它提供了这两个领域方法的详细概述。许多被调查的技术是独立于分类器的。然而，我们选择专注于基于决策树或与决策树兼容的技术。这种选择是基于它们在这两个领域的应用的流行性和新颖性。