The hierarchical classification with an imbalance class problem is a challenge for in machine learning, and is caused by data with an uneven distribution. Learning from an imbalanced dataset can lead to performance degradation of the classifier. Cost-sensitive learning is a useful solution for handling the gap probability of majority and minority classes. This paper proposes a cost-sensitive hierarchical classification for imbalance classes (CSHCIC), constructing a cost-sensitive factor to balance the relationship between majority and minority classes. First, we divide a large hierarchical classification task into several small subclassification tasks by class hierarchy. Second, we establish a cost-sensitive factor by more precisely using the number of different samples of subclassifications. Then, we calculate the probability of every node using logistic regression. Lastly, we update the cost-sensitive factor using the flexibility factor and the number of samples. The experimental results show that the cost-sensitive hierarchical classification method achieves excellent performance on handling imbalance class datasets. The running time cost of the proposed method is smaller than most state-of-the-art methods.

带有不平衡类问题的分层分类是机器学习中的一个挑战，它是由分布不均的数据引起的。从不平衡的数据集中学习可能会导致分类器的性能下降。成本敏感型学习是处理多数和少数族裔差距可能性的有用解决方案。本文提出了一种成本敏感的不平衡类别的分层分类法（CSHCIC），构造了一个成本敏感的因素来平衡多数和少数族裔类别之间的关系。首先，我们按类层次结构将大型分层分类任务划分为几个小型子分类任务。其次，我们通过更精确地使用不同子类别样本的数量来建立成本敏感因素。然后，我们使用逻辑回归计算每个节点的概率。最后，我们使用灵活性因子和样本数量来更新成本敏感因子。实验结果表明，该成本敏感的层次分类方法在处理不平衡类数据集方面表现优异。所提出的方法的运行时间成本小于大多数最新技术。