Counterfactual-based minority oversampling for imbalanced classification

Publication Name

Engineering Applications of Artificial Intelligence


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. Therefore, most of them are located near the decision boundary. The empirical evaluation of the six benchmark datasets shows that our approach clearly outperforms the state-of-the-art methods.

Open Access Status

This publication may be available as open access



Article Number


Funding Number


Funding Sponsor

National Natural Science Foundation of China



Link to publisher version (DOI)