Classifiers for a highly imbalanced dataset tend to bias in majority classes and, as a result, the minority class samples are usually misclassified as majority class. To overcome this, a proper undersampling technique that removes some majority samples can be an alternative. We propose an efficient and simple undersampling method for imbalanced datasets and show that the proposed method outperforms others with respect to four different performance measures by several illustrative experiments, especially for highly imbalanced datasets.

中文翻译：

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一种基于成员概率的不平衡数据欠采样算法

高度不平衡数据集的分类器倾向于偏向多数类，因此，少数类样本通常被错误分类为多数类。为了克服这个问题，可以采用适当的欠采样技术来去除一些多数样本。我们为不平衡数据集提出了一种有效且简单的欠采样方法，并通过几个说明性实验表明所提出的方法在四种不同的性能指标方面优于其他方法，尤其是对于高度不平衡的数据集。