Ensemble Generative Adversarial Imputation Network with Selective Multi-Generator (ESM-GAIN) for Missing Data Imputation

被引:4
|
作者
Li, Yuxuan [1 ]
Dogan, Ayse [1 ]
Liu, Chenang [1 ]
机构
[1] Oklahoma State Univ, Sch Ind Engn & Management, Stillwater, OK 74078 USA
来源
2022 IEEE 18TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE) | 2022年
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
Ensemble learning; GAIN; missing data imputation; multi-generator generation; DIAGNOSIS;
D O I
10.1109/CASE49997.2022.9926629
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As a pervasive issue, missing data may influence the data modeling performance and lead to more difficulties of completing the desired tasks. Many approaches have been developed for missing data imputation. Recently, by taking advantage of the emerging generative adversarial network (GAN), an effective missing data imputation approach termed generative adversarial imputation nets (GAIN) was developed. However, its modeling architecture may still lead to significant imputation bias. In addition, with the GAN structure, the training process of GAIN may be instable and the imputation variation may be high. Hence, to address these two limitations, the ensemble GAIN with selective multi-generator (ESM-GAIN) is proposed to improve the imputation accuracy and robustness. The contributions of the proposed ESM-GAIN consist of two aspects: (1) a selective multi- generation framework is proposed to identify high-quality imputations; (2) an ensemble learning framework is incorporated for GAIN imputation to improve the imputation robustness. The effectiveness of the proposed ESM-GAIN is validated by both numerical simulation and two realworld breast cancer datasets.
引用
收藏
页码:807 / 812
页数:6
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