Counterfactual-based minority oversampling for imbalanced classification

by   Hao Luo, et al.

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.


page 2

page 8


M2m: Imbalanced Classification via Major-to-minor Translation

In most real-world scenarios, labeled training datasets are highly class...

Remix: Rebalanced Mixup

Deep image classifiers often perform poorly when training data are heavi...

Distributionally Robust Counterfactual Risk Minimization

This manuscript introduces the idea of using Distributionally Robust Opt...

The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification

The problem of class imbalanced data lies in that the generalization per...

UGRWO-Sampling: A modified random walk under-sampling approach based on graphs to imbalanced data classification

In this paper, we propose a new RWO-Sampling (Random Walk Over-Sampling)...

Automated Imbalanced Classification via Layered Learning

In this paper we address imbalanced binary classification (IBC) tasks. A...

Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text

Machine Learning has seen tremendous growth recently, which has led to a...