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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국인터넷방송통신학회 International journal of internet, broadcasting and communication : IJIBC International journal of internet, broadcasting and communication : IJIBC 제11권 제4호
- 발행연도
- 2019.1
- 수록면
- 37 - 42 (6page)
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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.
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참고문헌 (11)
참고문헌 신청
Bartosz Krawczyk / 2016 / Learning from imbalanced data: open challenges and future directions / Progress in Artificial Intelligence 5(4):221~232
C. X. Ling / 1998 / Data Mining for Direct Marketing: Problems and Solutions / KDD 98:73~79
N. V. Chawla / 2002 / SMOTE: Synthetic Minority Over-sampling Technique / Journal of Artificial Intelligence Research 16:321~357
H. He / 2008 / ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning / 2008 IEEE International Joint Conference on Neural Networks :1322~1328
I. Goodfellow / 2014 / Generative Adversarial Nets / Proc. Neural Information Processing Systems 2014 :2672~2680
C. X. Ling / 1998 / Data Mining for Direct Marketing: Problems and Solutions / KDD 98:73~79
N. V. Chawla / 2002 / SMOTE: Synthetic Minority Over-sampling Technique / Journal of Artificial Intelligence Research 16:321~357
H. He / 2008 / ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning / 2008 IEEE International Joint Conference on Neural Networks :1322~1328
I. Goodfellow / 2014 / Generative Adversarial Nets / Proc. Neural Information Processing Systems 2014 :2672~2680
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