Synthetic minority oversampling of vital statistics data with generative adversarial networks.
Minority oversampling is a standard approach used for adjusting the ratio between the classes on imbalanced data. However, established methods often provide modest improvements in classification performance when applied to data with extremely imbalanced class distribution and to mixed-type data. This is usual for vital statistics data, in which the outcome incidence dictates the amount of positive observations. In this article, we developed a novel neural network-based oversampling method called [...]
Author(s): Koivu, Aki, Sairanen, Mikko, Airola, Antti, Pahikkala, Tapio
DOI: 10.1093/jamia/ocaa127