Supervised Class Distribution Learning for GANs-Based Imbalanced Classification

Abstract

Class imbalance is a challenging problem in many real-world applications such as fraudulent transactions detection in finance and diagnosis of rare diseases in medicine, which has attracted more and more attention in the community of machine learning and data mining. The main issue is how to capture the fundamental characteristics of the imbalanced data distribution. In particular, whether the hidden pattern can be truly mined from minority class is still a largely unanswered question after all it contains limited instances. The existing methods provide only a partial understanding of this issue and result in the biased and inaccurate classifiers. To overcome this issue, we propose a novel imbalanced classification framework with two stages. The first stage aims to accurately determine the class distributions by a supervised class distribution learning method under the Wasserstein auto-encoder framework. The second stage makes use of the generative adversarial networks to simultaneously generate instances according to the learnt class distributions and mine the discriminative structure among classes to train the final classifier. This proposed framework focuses on Supervised Class Distribution Learning for Generative Adversarial Networks-based imbalanced classification (SCDL-GAN). By comparing with the state-of-the-art methods, the experimental results demonstrate that SCDL-GAN consistently benefits the imbalanced classification task in terms of several widely-used evaluation metrics on five benchmark datasets.