Autoencoder-based Attribute Noise Handling Method for Medical Data
This addresses a critical data quality issue for medical data analysis, though it appears incremental as it builds on autoencoder techniques.
The paper tackles attribute noise in medical datasets by proposing an autoencoder-based preprocessing method, which outperforms existing imputation and noise correction methods on real-world medical datasets.
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.