Web: http://arxiv.org/abs/2206.08478

June 20, 2022, 1:10 a.m. | Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramon Vinas Torne, Evis Sala, Pietro L

cs.LG updates on arXiv.org arxiv.org

Classifying samples in incomplete datasets is a common aim for machine
learning practitioners, but is non-trivial. Missing data is found in most
real-world datasets and these missing values are typically imputed using
established methods, followed by classification of the now complete, imputed,
samples. The focus of the machine learning researcher is then to optimise the
downstream classification performance. In this study, we highlight that it is
imperative to consider the quality of the imputation. We demonstrate how the
commonly used …

arxiv classification datasets imputation lg missing values values

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY