Page 36 - Kỷ yếu hội thảo khoa học lần thứ 12 - Công nghệ thông tin và Ứng dụng trong các lĩnh vực (CITA 2023)
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Fig. 1. The RMSE performance with various missing rates
5 Conclusion
In this paper, we present a different method to handle the missing data named Evolu-
tionary Generative Adversarial for Imputation Data (EGAIN). This novel framework
takes advantage of GAN frameworks and evolutionary computation paradigm, which
allows more than individual adversarial objectives, and selects the best candidate for
each training iteration. Experiments highlights the good performance of EGAIN against
other methods.
References
1. M. Arjovsky and L. Bottou. Towards principled methods for training generativeadversarial
networks. arXiv preprint arXiv:1701.04862, 2017.
CITA 2023 ISBN: 978-604-80-8083-9