MIVAE: Multiple Imputation based on Variational Auto-Encoder

插补(统计学) 计算机科学 数据挖掘 缺少数据 编码器 人工智能 机器学习 操作系统
作者
Qian Ma,Xia Li,Mei Bai,Xite Wang,Bo Ning,Guanyu Li
出处
期刊:Engineering Applications of Artificial Intelligence [Elsevier]
卷期号:123: 106270-106270 被引量:6
标识
DOI:10.1016/j.engappai.2023.106270
摘要

Nowadays, the issue of MV imputation has become one of the research hotspots in the field of data quality, since the missing values (MVs) are prevalent in real-world datasets and bring challenges to advanced data analytics algorithms. To impute the MVs, most existing approaches directly derive one estimation for each MV, which is categorized as the single imputation (SI). However, the SI ignores the uncertainty of the MVs, and thereby usually derive unsatisfactory imputation results compared to the Multiple imputation (MI). To extract the uncertainty of the MVs, the MI algorithms derive multiple candidate estimations for each MV. Nevertheless, existing MI approaches are few due to the complicated data-handling process. Accordingly, in this paper, by exploring the Variational Auto-Encoder (VAE) model, we propose a new MI approach, namely MIVAE (Multiple Imputation based on Variational Auto-Encoder) to impute MVs for the tabular data. In MIVAE, we first add a corrupted input layer (where the synthetic MVs are introduced) adjacent to the original input layer to make the model capable of MV issue. Then, we obtain multiple rather than single candidate estimations for each data sample from the posterior distribution of the latent variables learned by our designed model. In such way, the multiple imputation is effectively implemented where the uncertainty of the MVs are extracted perfectly. Next, to obtain satisfactory imputation results, we add a data analysis layer at the end of the network to integrate multiple candidate estimations intelligently. Finally, the experimental results over four real-world datasets demonstrate that MIVAE achieves significantly higher imputation accuracy compared to existing solutions, and MIVAE are capable of handling both numerical and categorized tabular data. For example, the imputation accuracy based on MIVAE improves up to about 40% and 30% compared with PMM and MIWAE (which are the state-of-the-art MI approach) over the CropMapping dataset, respectively. Moreover, we train a MIVAE model over three datasets containing MVs, respectively. By leveraging the trained MIVAE, the classification performance over the imputed data is similar to that over the complete data.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
123完成签到,获得积分10
刚刚
冻冻妖完成签到,获得积分10
刚刚
太阳完成签到,获得积分10
1秒前
犹豫的怀蝶完成签到,获得积分10
1秒前
wyg1994发布了新的文献求助10
2秒前
狸宝的小果子完成签到 ,获得积分10
2秒前
2秒前
汉堡包应助米娅采纳,获得20
3秒前
龘龘完成签到,获得积分10
3秒前
chengzi完成签到,获得积分10
3秒前
3秒前
4秒前
莹亮的星空完成签到,获得积分0
5秒前
852应助学术地瓜采纳,获得10
5秒前
所所应助荼蘼采纳,获得10
5秒前
Z赵完成签到 ,获得积分10
5秒前
RUI完成签到,获得积分10
5秒前
zyf完成签到,获得积分10
6秒前
小白菜完成签到,获得积分10
6秒前
学呀学发布了新的文献求助10
7秒前
IBMffff应助APS采纳,获得10
7秒前
8秒前
dwls应助sclslc采纳,获得30
9秒前
9秒前
温婉的乌完成签到,获得积分10
10秒前
鹿七七完成签到,获得积分10
10秒前
张勇涛完成签到,获得积分10
10秒前
WWW完成签到,获得积分10
11秒前
11秒前
11秒前
Michael发布了新的文献求助10
11秒前
不朽阳神完成签到,获得积分10
11秒前
深情安青应助jja采纳,获得10
11秒前
rain完成签到,获得积分20
12秒前
今天看文献了吗完成签到,获得积分10
12秒前
研友_knggYn完成签到,获得积分0
12秒前
12秒前
青鱼完成签到,获得积分10
14秒前
田様应助LL采纳,获得10
14秒前
凶狠的斓发布了新的文献求助10
14秒前
高分求助中
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
Smart but Scattered: The Revolutionary Executive Skills Approach to Helping Kids Reach Their Potential (第二版) 1000
Very-high-order BVD Schemes Using β-variable THINC Method 850
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 800
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 800
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3249148
求助须知:如何正确求助?哪些是违规求助? 2892506
关于积分的说明 8272098
捐赠科研通 2560817
什么是DOI,文献DOI怎么找? 1389243
科研通“疑难数据库(出版商)”最低求助积分说明 651047
邀请新用户注册赠送积分活动 627889