Work

Imbalanced Data Classification via Cooperative Interaction Between Classifier and Generator

Year: 2021

Type: article

Abstract: Learning classifiers with imbalanced data can be strongly biased toward the majority class. To address this issue, several methods have been proposed using generative adversarial networks (GANs). Existing GAN-based methods, however, do not effectively utilize the relationship between a classifier and a generator. This article proposes a novel three-player structure consisting of a discriminator, a generator, and a classifier, along with decision boundary regularization. Our method is distinctive in which the generator is trained in cooperation with the classifier to provide minority samples that gradually expand the minority decision region, improving performance for imbalanced data classification. The proposed method outperforms the existing methods on real data sets as well as synthetic imbalanced data sets.

Source: IEEE Transactions on Neural Networks and Learning Systems

DOI: 10.1109/tnnls.2021.3052243

Authors: Hyun-Soo Choi, Dahuin Jung, Siwon Kim, Sungroh Yoon

Institutions: SK Group (South Korea), Seoul National University, Kangwon National University, Institute of Engineering Research

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $2,045