Page 37 - Kỷ yếu hội thảo khoa học lần thứ 12 - Công nghệ thông tin và Ứng dụng trong các lĩnh vực (CITA 2023)
P. 37
Bao Ngoc Vi, Cao Truong Tran, Chi Cong Nguyen 21
2. M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial net-works. In
International conference on machine learning, pages 214 223. PMLR,2017
3. S. Arora, R. Ge, Y. Liang, T. Ma, and Y. Zhang. Generalization and equilibriumin generative
adversarial nets (gans). In International Conference on MachineLearning, pages 224 232.
PMLR, 2017.
4. A. Asuncion and D. Newman. UCI machine learning repository, 2007.
5. X. Y. Chaoyue Wang, Chang Xu and D. Tao. Evolutionary generative adversarialnetworks.
arXiv preprint arXiv:1803.00657, 2018.
6. P. J. Garc -Laencina, J.-L. Sancho-G omez, and A. R. Figueiras-Vidal. Patternclassifica-
tion with missing data: a review. Neural Computing and Applications,19:263 282, 2010.
7. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair,A. Courville,
and Y. Bengio. Generative adversarial nets. In Z. Ghahramani,M. Welling, C. Cortes, N.
Lawrence, and K. Q. Weinberger, editors, Advances inNeural Information Processing Sys-
tems, volume 27. Curran Associates, Inc., 2014.
8. J. M. Jerez, I. Molina, P. J. Garc -Laencina, E. Alba, N. Ribelles, M. Mart
Missing data imputation using statistical and machine learning methodsin a real breast can-
cer problem. Artificial intelligence in medicine, 50:105 115,2010.
9. X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. Paul Smolley. Least squares gen-erative
adversarial networks. In Proceedings of the IEEE international conferenceon computer vi-
sion, pages 2794 2802, 2017.
10. V. Nagarajan and J. Z. Kolter. Gradient descent gan optimization is locally stable.arXiv pre-
print arXiv:1706.04156, 2017.
11. A. Nguyen, J. Clune, Y. Bengio, A. Dosovitskiy, and J. Yosinski. Plug & playgenerative
networks: Conditional iterative generation of images in latent space. InProceedings of the
IEEE Conference on Computer Vision and Pattern Recognition,pages 4467 4477, 2017.
12. T. D. Nguyen, T. Le, H. Vu, and D. Phung. Dual discriminator generative adver-sarial nets.
arXiv preprint arXiv:1709.03831, 2017.
13. J. R. Quinlan. C4. 5: programs for machine learning. Elsevier, 2014.
14. J. A. C. Sterne, I. R. White, J. B. Carlin, M. Spratt, P. Royston, M. G. Ken-ward, A. M.
Wood, and J. R. Carpenter. Multiple imputation for missing datain epidemiological and clin-
ical research: potential and pitfalls. Bmj, 338(jun29 1),2009.
15. B. M. M. Steven Cheng-Xian Li, Bo Jiang. Misgan: Learning from incomplete datawith
generative adversarial networks. arXiv preprint arXiv:1902.09599, 2019.
16. D. Talwar, A. Mongia, D. Sengupta, and A. Majumdar. Autoimpute: Autoencoderbased im-
putation of single-cell rna-seq data. Scientific Reports, 8(1), 2018
17. C. T. Tran, M. Zhang, and P. Andreae. Multiple imputation for missing data usinggenetic
programming. In Proceedings of the 2015 Annual Conference on Geneticand Evolutionary
590, New York, NY, USA,2015. Association for
Computing Machinery.
18. C. T. Tran, M. Zhang, P. Andreae, and B. Xue. Multiple imputation and geneticprogram-
ming for classification with incomplete data. In Proceedings of the Geneticand Evolutionary
Computation Conference, pages 521 528, 2017.
19. N. Tsikriktsis. A review of techniques for treating missing data in om surveyresearch. Jour-
nal of Operations Management, 24:53 62, 2005.
20. I. Yeh. Uci machine learning repository: Data set. Irvine: University of California,2007.
21. J. Yoon, J. Jordon, and M. van der Schaar. GAIN: missing data imputation usinggenerative
adversarial nets. CoRR, abs/1806.02920, 2018.
ISBN: 978-604-80-8083-9 CITA 2023