Adaptive learning of minority class prior to minority oversampling
Pattern Recognition Letters
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.
Sadhukhan, Payel and Palit, Sarbani, "Adaptive learning of minority class prior to minority oversampling" (2020). Journal Articles. 190.