Handling Missing Values via a Neural Selective Input Model

Abstract

Missing data represent an ubiquitous problem with numerous and diverse causes. Handling Missing Values (MVs) properly is a crucial issue, in particular in Machine Learning (ML) and pattern recognition. To date, the only option available for standard Neural Networks (NNs) to handle this problem has been to rely on pre-processing techniques such as imputation… (More)

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