The task of learning from imbalanced datasets has been widely investigated in the binary, multi-class and multi-label classification scenarios. Although this problem also affects hierarchical datasets, there are few work in the literature dealing with it. Meanwhile, the local classifier approaches are the most used techniques in the literature to deal with Hierarchical Classification problems. In this paper, we present new ways to handle data imbalance in hierarchical classification problems when using local classifiers approaches. We propose three different resampling schemas, according to the local classification approach: (1) Local Classifiers per Node; (2) Local Classifiers per Parent Node; and (3) Local Classifiers per Level. In order to define how imbalanced a certain hierarchical dataset is, we also propose three novel metrics to measure the imbalance in hierarchical datasets considering the different local classification approaches. The experimental evaluation in eight well-known datasets showed that the imbalance metrics can indeed measure the datasets imbalance and the proposed resampling schemas are able to improve the classification results when compared to baselines, state-of-the-art and related work approaches.

从不平衡数据集学习的任务已在二进制，多类和多标签分类方案中得到了广泛研究。尽管此问题也影响分层数据集，但文献中很少涉及此问题。同时，局部分类器方法是文献中用于处理分层分类问题的最常用技术。在本文中，我们提出了使用局部分类器方法处理分层分类问题中数据不平衡的新方法。根据本地分类方法，我们提出了三种不同的重采样方案：（1）每个节点的本地分类器；（2）每个父节点的本地分类器；（3）每个级别的本地分类器。为了定义某个分层数据集的不平衡程度，考虑到不同的本地分类方法，我们还提出了三种新颖的度量标准来衡量分层数据集中的不平衡。对八个著名数据集的实验评估表明，失衡指标确实可以衡量数据集的失衡，并且与基线，最新技术和相关工作方法相比，提出的重采样方案能够改善分类结果。