{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T03:34:51Z","timestamp":1775446491788,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T00:00:00Z","timestamp":1714780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Due to various reasons, such as limitations in data collection and interruptions in network transmission, gathered data often contain missing values. Existing state-of-the-art generative adversarial imputation methods face three main issues: limited applicability, neglect of latent categorical information that could reflect relationships among samples, and an inability to balance local and global information. We propose a novel generative adversarial model named DTAE-CGAN that incorporates detracking autoencoding and conditional labels to address these issues. This enhances the network\u2019s ability to learn inter-sample correlations and makes full use of all data information in incomplete datasets, rather than learning random noise. We conducted experiments on six real datasets of varying sizes, comparing our method with four classic imputation baselines. The results demonstrate that our proposed model consistently exhibited superior imputation accuracy.<\/jats:p>","DOI":"10.3390\/e26050402","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T15:05:01Z","timestamp":1715007901000},"page":"402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Detracking Autoencoding Conditional Generative Adversarial Network: Improved Generative Adversarial Network Method for Tabular Missing Value Imputation"],"prefix":"10.3390","volume":"26","author":[{"given":"Jingrui","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"},{"name":"Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400044, China"}]},{"given":"Zixin","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China"}]},{"given":"Xinkai","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}]},{"given":"Jingxuan","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1792-1378","authenticated-orcid":false,"given":"Yunfei","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,4]]},"reference":[{"key":"ref_1","first-page":"402","article-title":"A method for filling missing values in aircraft fuel data based on generative adversarial networks","volume":"48","author":"Guo","year":"2021","journal-title":"J. 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