Towards Better Modeling With Missing Data : A Contrastive Learning-Based Visual Analytics Perspective
Missing data can pose a challenge for machine learning (ML) modeling. To address this, current approaches are categorized into feature imputation and label prediction and are primarily focused on handling missing data to enhance ML performance. These approaches rely on the observed data to estimate...
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Détails bibliographiques
| Publié dans: | IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 8 vom: 13. Aug., Seite 5129-5146
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| Auteur principal: |
Xie, Laixin
(Auteur) |
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| Autres auteurs: |
Ouyang, Yang,
Chen, Longfei,
Wu, Ziming,
Li, Quan |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2024
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| Accès à la collection: | IEEE transactions on visualization and computer graphics
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| Sujets: | Journal Article |
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