  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 methods and noise
correction methods on several real-world medical datasets.
