Many sampling-based preprocessing methods have been proposed to solve the problem of unbalanced dataset classification. The fundamental principle of these methods is rebalancing an unbalanced dataset by a concrete strategy. Herein, we introduce a novel hybrid proposal named ant colony optimization resampling (ACOR) to overcome class imbalance classification. ACOR primarily includes two steps: first, it rebalances an imbalanced dataset by a specific oversampling algorithm; next, it finds an (sub)optimal subset from the balanced dataset by ant colony optimization. Unlike other oversampling techniques, ACOR does not focus on the mechanics of generating new samples. The main advantage of ACOR is that existing oversampling algorithms can be fully utilized and an ideal training set can be obtained by ant colony optimization. Therefore, ACOR can enhance the performance of existing oversampling algorithms. Experimental results on 18 real imbalanced datasets prove that ACOR yields significantly better results compared with four popular oversampling methods in terms of various assessment metrics, such as AUC, G-mean, and BACC.

已经提出了许多基于采样的预处理方法来解决数据集分类不平衡的问题。这些方法的基本原理是通过具体策略重新平衡不平衡的数据集。在这里，我们介绍了一种新颖的混合提案，称为蚁群优化重采样（ACOR），以克服类别不平衡分类。ACOR主要包括两个步骤：首先，它通过特定的过采样算法对不平衡的数据集进行重新平衡；接下来，它通过蚁群优化从平衡数据集中找到一个（次）最优子集。与其他过采样技术不同，ACOR并不专注于生成新样本的机制。ACOR的主要优点是可以充分利用现有的过采样算法，并且可以通过蚁群优化获得理想的训练集。因此，ACOR可以增强现有过采样算法的性能。在18个真实不平衡数据集上的实验结果证明，与AUC，G-mean和BACC等各种评估指标相比，ACOR的结果要比四种流行的过采样方法好得多。