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(2023) Learning from data with structured missingness. Nature Machine Intelligence, 5 . pp. 13-23. doi:10.1038/s42256-022-00596-z ISSN 2522-5839.
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WRAP-Learning-data-structured-missingness-22.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 25 July 2023. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (664Kb) |
Official URL: https://doi.org/10.1038/s42256-022-00596-z
Abstract
Missing data are an unavoidable complication in many machine learning tasks. When data are ‘missing at random’ there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an increasingly encountered problem arises in which missing values exhibit an association or structure, either explicitly or implicitly. Such ‘structured missingness’ raises a range of challenges that have not yet been systematically addressed, and presents a fundamental hindrance to machine learning at scale. Here, we outline the current literature and propose a set of grand challenges in learning from data with structured missingness.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Social Sciences > Warwick Business School |
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| Journal or Publication Title: | Nature Machine Intelligence | ||||||
| Publisher: | Springer | ||||||
| ISSN: | 2522-5839 | ||||||
| Official Date: | 25 January 2023 | ||||||
| Dates: |
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| Volume: | 5 | ||||||
| Page Range: | pp. 13-23 | ||||||
| DOI: | 10.1038/s42256-022-00596-z | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1038/s42256-022-00596-z. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/acceptedmanuscript-terms. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 22 November 2022 | ||||||
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