Learning from data with structured missingness

Autor:	Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti, Chris Holmes, Ryan Copping, Niels Hagenbuch, Stefanie Biedermann, Jack Noonan, Brieuc Lehmann, Aditi Shenvi, Xuan Vinh Doan, David Leslie, Ginestra Bianconi, Ruben Sanchez-Garcia, Alisha Davies, Maxine Mackintosh, Eleni-Rosalina Andrinopoulou, Anahid Basiri, Chris Harbron, Ben D. MacArthur
Rok vydání:	2023
Předmět:	FOS: Computer and information sciences Human-Computer Interaction Computer Science - Machine Learning Statistics - Machine Learning Artificial Intelligence Computer Networks and Communications Machine Learning (stat.ML) Computer Vision and Pattern Recognition Software Machine Learning (cs.LG)
Zdroj:	Nature Machine Intelligence. 5:13-23
ISSN:	2522-5839
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a495d3108e8f51b48323ec63f38070a2 https://doi.org/10.1038/s42256-022-00596-z Zobrazit plný text záznamu Full text from SpringerLink