The minority oversampling techniques have substantiated their appropriateness and utility in the domain of class-imbalance learning. However, this does not affirm the true class of the synthetic minority points. In this work, Adaptive Learning of Minority Class prior to Minority Oversampling (ALMCMO), we work towards bridging this gap by estimating the minority set before oversampling the synthetic points. We estimate a varying and adaptive volume of minority space around the minority points. We aim to guarantee the class-memberships of the synthetic minority points by sampling them from the estimated minority spaces. In our empirical study, we have used six comparing methods, 23 datasets and two classifiers. The results indicate the certain superiority of the proposed method over the six competing schemes.

少数人过度采样技术已经证实了它们在班级不平衡学习领域的适当性和实用性。但是，这并不能确定综合少数派积分的真实类别。在这项工作中，少数群体过采样之前的适应性学习少数群体（ALMCMO），我们通过在对综合点进行过采样之前估计少数群体来努力弥合这一差距。我们估计了少数人点周围少数人空间的变化和自适应量。我们的目标是通过从估计的少数族裔空间中对它们进行采样，来保证它们的类成员资格。在我们的实证研究中，我们使用了六种比较方法，23个数据集和两个分类器。结果表明，所提出的方法相对于六个竞争方案具有一定的优势。