  A key challenge of oversampling in imbalanced classification is that the
generation of new minority samples often neglects the usage of majority
classes, resulting in most new minority sampling spreading the whole minority
space. In view of this, we present a new oversampling framework based on the
counterfactual theory. Our framework introduces a counterfactual objective by
leveraging the rich inherent information of majority classes and explicitly
perturbing majority samples to generate new samples in the territory of
minority space. It can be analytically shown that the new minority samples
satisfy the minimum inversion, and therefore most of them locate near the
decision boundary. Empirical evaluations on benchmark datasets suggest that our
approach significantly outperforms the state-of-the-art methods.
