Web: http://arxiv.org/abs/2206.10609

June 23, 2022, 1:10 a.m. | Thomas Ranvier (LIRIS, DM2L), Haytham Elgazel (LIRIS, DM2L), Emmanuel Coquery (LIRIS), Khalid Benabdeslem (LIRIS, DM2L)

cs.LG updates on arXiv.org arxiv.org

Medical datasets are particularly subject to attribute noise, that is,
missing and erroneous values. Attribute noise is known to be largely
detrimental to learning performances. To maximize future learning performances
it is primordial to deal with attribute noise before any inference. We propose
a simple autoencoder-based preprocessing method that can correct mixed-type
tabular data corrupted by attribute noise. No other method currently exists to
handle attribute noise in tabular data. We experimentally demonstrate that our
method outperforms both state-of-the-art imputation …

arxiv autoencoder data lg medical noise

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