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Experimental Analysis of Equilibrization in Binary Classification for Non-Image Imbalanced Data Using Wasserstein GAN

원문정보

초록

영어

In this paper, we explore the details of three classic data augmentation methods and two generative model based oversampling methods. The three classic data augmentation methods are random sampling (RANDOM), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The two generative model based oversampling methods are Conditional Generative Adversarial Network (CGAN) and Wasserstein Generative Adversarial Network (WGAN). In imbalanced data, the whole instances are divided into majority class and minority class, where majority class occupies most of the instances in the training set and minority class only includes a few instances. Generative models have their own advantages when they are used to generate more plausible samples referring to the distribution of the minority class. We also adopt CGAN to compare the data augmentation performance with other methods. The experimental results show that WGAN-based oversampling technique is more stable than other approaches (RANDOM, SMOTE, ADASYN and CGAN) even with the very limited training datasets. However, when the imbalanced ratio is too small, generative model based approaches cannot achieve satisfying performance than the conventional data augmentation techniques. These results suggest us one of future research directions.

목차

Abstract
1. Introduction
2. Related Work
3. GAN Topologies for Experiments
4. Experimental ResultsWe have performed over
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCE

저자정보

  • Zhi-Yong Wang Weifang University of Science and Technology, China, Department of Computer Engineering, Dongseo University, Busan, Korea
  • Dae-Ki Kang Department of Computer Engineering, Dongseo University, Busan, Korea

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