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Imputation using training labels and classification via label imputation
April 24, 2024, 4:43 a.m. | Thu Nguyen, Tuan L. Vo, P{\aa}l Halvorsen, Michael A. Riegler
cs.LG updates on arXiv.org arxiv.org
Abstract: Missing data is a common problem in practical settings. Various imputation methods have been developed to deal with missing data. However, even though the label is usually available in the training data, the common practice of imputation usually only relies on the input and ignores the label. In this work, we illustrate how stacking the label into the input can significantly improve the imputation of the input. In addition, we propose a classification strategy that …
abstract arxiv classification cs.lg data deal however imputation labels practical practice stat.ml training training data type via
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