M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values

arXiv:2405.00182v1 Announce Type: new
Abstract: Missing value imputation is a crucial preprocessing step for many machine learning problems. However, it is often considered as a separate subtask from downstream applications such as classification, regression, or clustering, and thus is not optimized together with them. We hypothesize that treating the imputation model and downstream task model together and optimizing over full pipelines will yield better results than treating them separately. Our work describes a novel AutoML technique for making downstream predictions …

abstract applications arxiv classification clustering cs.ai cs.lg dynamic ensemble however imputation machine machine learning missing values regression them together type value values