Abstract: Classification algorithms face significant challenges when dealing with imbalanced datasets.Oversampling methods,which synthesize new samples to balance the sample sizes across classes,have emerged as a mainstream solution.However,exis-ting approaches exhibit notable limitations in terms of the quality of generated samples,model over-generalization under high imbalance ratios,and instability in feature learning.To address these issues,this paper proposes an adversarial learning-based oversampling method with dual adaptive constraints.The method consists of two components:a joint adversarial learning framework and dual adaptive constraints.Firstly,the joint adversarial learning framework employs variational autoencoders and discriminators to form an adversarial training process,enhancing the quality and diversity of the generated samples.Secondly,differential adaptive and standard adaptive constraints are collaboratively applied to filter samples,preventing over-generalization and unstable feature learning in highly imbalanced datasets.This ensures that the generated samples progressively approximate the real distribution and are stably synthesized.Experimental results show that the proposed method ranks first on average in terms of F1-Score,G-mean,and AUC,achieving an average improvement of 16.8% in G-mean on highly imbalanced datasets.The proposed method effectively enhances sample generation quality and feature representation capability,providing an effective solution to key problems in imbalanced data classification.

Key words: Imbalanced data classification, Feature learning stability, Dual adaptive constraints, Variational autoencoder, Adversarial learning