Missing Data Imputation Using Data Generated by GAN

Hanan Hammad Alharbi, Masaomi Kimura

研究成果: Conference contribution

抄録

Missing data is a common and challenging problem that arises in many research domains and led to the complication of data analysis. Therefore, handling missing data is a necessity as proposed in many previous studies. In this paper, we proposed two methods to impute missing numerical datasets based on generated data by GAN and determine the imputed values using Euclidian distance. In various missing percentages, we evaluated the imputation accuracy of all methods using MAE and RMSE tests. The proposed methods randomGAN and meshGAN produce the best imputation accuracy in 2 out of 4 datasets against three compared methods: SimpleImputer, IterativeImputer, and KNNimputer.

本文言語English
ホスト出版物のタイトルICCBD 2020 - 2020 3rd International Conference on Computing and Big Data
ホスト出版物のサブタイトルWorkshop 2020 2nd International Conference on Computer, Software Engineering and Applications, CSEA 2020
出版社Association for Computing Machinery
ページ73-77
ページ数5
ISBN(電子版)9781450387866
DOI
出版ステータスPublished - 2020 8 5
イベント3rd International Conference on Computing and Big Data, ICCBD 2020 and its Workshop the 2020 2nd International Conference on Computer, Software Engineering and Applications, CSEA 2020 - Virtual, Online, Taiwan, Province of China
継続期間: 2020 8 52020 8 7

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Computing and Big Data, ICCBD 2020 and its Workshop the 2020 2nd International Conference on Computer, Software Engineering and Applications, CSEA 2020
CountryTaiwan, Province of China
CityVirtual, Online
Period20/8/520/8/7

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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