Adversarial Classifier for Imbalanced Problems

Abstract

Adversarial approach has been widely used for data generation in the last few years. However, this approach has not been extensively utilized for classifier training. In this paper, we propose an adversarial framework for classifier training that can also handle imbalanced data. Indeed, a network is trained via an adversarial approach to give weights to samples of the majority class such that the obtained classification problem becomes more challenging for the discriminator and thus boosts its classification capability. In addition to the general imbalanced classification problems, the proposed method can also be used for problems such as graph representation learning in which it is desired to discriminate similar nodes from dissimilar nodes. Experimental results on imbalanced data classification and on the tasks like graph link prediction show the superiority of the proposed method compared to the state-of-the-art methods.

References in corpus (8)

• SMOTE: Synthetic Minority Over-sampling Technique
• Semi-Supervised Classification with Graph Convolutional Networks
• Variational Graph Auto-Encoders
• BAGAN: Data Augmentation with Balancing GAN
• GraphGAN: Graph Representation Learning with Generative Adversarial Nets
• Neural Embeddings of Graphs in Hyperbolic Space
• Deep Over-sampling Framework for Classifying Imbalanced Data
• Adversarial Training for Community Question Answer Selection Based on Multi-scale Matching

Cited by in corpus (4)

• Fairness without Demographics through Adversarially Reweighted Learning
• Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks
• ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
• Contrastive Language Adaptation for Cross-Lingual Stance Detection