دورية أكاديمية
Learning from data with structured missingness
| العنوان: | Learning from data with structured missingness |
|---|---|
| المؤلفون: | Mitra, R, McGough, SF, Chakraborti, T, Holmes, C, Copping, R, Hagenbuch, N, Biedermann, S, Noonan, J, Lehmann, B, Shenvi, A, Doan, XV, Leslie, D, Bianconi, G, Sanchez-Garcia, R, Davies, A, Mackintosh, M, Andrinopoulou, ER, Basiri, A, Harbron, C, MacArthur, BD |
| المصدر: | Nature Machine Intelligence , 5 pp. 13-23. (2023) |
| بيانات النشر: | NATURE PORTFOLIO |
| سنة النشر: | 2023 |
| المجموعة: | University College London: UCL Discovery |
| مصطلحات موضوعية: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Interdisciplinary Applications, MULTIPLE IMPUTATION, INFERENCE |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | text |
| اللغة: | English |
| العلاقة: | https://discovery.ucl.ac.uk/id/eprint/10166739/1/main_revised_no_orange.pdfTest; https://discovery.ucl.ac.uk/id/eprint/10166739Test/ |
| الإتاحة: | https://discovery.ucl.ac.uk/id/eprint/10166739/1/main_revised_no_orange.pdfTest https://discovery.ucl.ac.uk/id/eprint/10166739Test/ |
| حقوق: | open |
| رقم الانضمام: | edsbas.68143390 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |