内容提示: GAIN: Missing Data Imputation using Generative Adversarial NetsJinsung Yoon 1 * James Jordon 2 * Mihaela van der Schaar 1 2 3AbstractWe propose a novel method for imputing missingdata by adapting the well-known Generative Ad-versarial Nets (GAN) framework. Accordingly,we call our ...
We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN). The generator (G) observes some components of a real data vector, imputes the missing component...
近期,ICML 2018收录了一篇GAN用于数据修复的文章,题为《GAIN: Missing Data Imputation using Generative Adversarial Nets》。在generator (G) 中,输入部分观测数据从而得到完备的数据,而determinator (D) 则用于判别哪些数据是观测到的、哪些是修复出来的。为了保证学习的有效性,新增了一个hint matrix用于指示缺失数据...
11 Commits GAIN_Letter.py GAIN_Spam.py Letter.csv MNST_Code_Example.py README.md Spam.csv Repository files navigation README Title: GAIN: Missing Data Imputation using Generative Adversarial Nets Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar ...
This paper presents three data imputation methods based on GAN: SGAIN, WSGAIN-CP and WSGAIN-GP. These methods were tested on datasets with different settings of missing values probabilities, where the values are missing completely at random (MCAR). The evaluation of the newly developed methods ...
Paper: Jinsung Yoon, James Jordon, Mihaela van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," International Conference on Machine Learning (ICML), 2018. Paper Link:http://proceedings.mlr.press/v80/yoon18a/yoon18a.pdf ...
(2018), we propose a novel generative model called PC-GAIN for missing data imputation. With the aid of an auxiliary classifier, which has been pre-trained using a subset of low-missing-rate samples and the corresponding pseudo-labels, the generator tries to produce indistinguishable imputation ...
3.5. Data preprocessing: mean and mode imputation, normalization, and data partitioning The absent values in the genuine dataset of heart ailment hinder its utilization in the prediction procedure. Moreover, records with absent values cannot be deleted because few of these records exist in the datas...
However, as the above analyses primarily attributed differences in BMI SDS to weight, further analyses were conducted based on weight SDS only, for which there were more complete data, with no imputation of missing data. We analysed the risk of rapid weight gain, defined a priori as an ...
Missing data were verified being completely missing at random by the use of Little’s test [18] with no imputation of missing data. Statistical power calculation was performed post hoc based on the assumption that the prevalence of AD was 11% in the first year of life [12]. A population ...