TITLE: Domain Adaptation in Highly Imbalanced and Overlapping Datasets 

ABSTRACT: In many machine learning domains, datasets are characterized by highly
imbalanced and overlapping classes. Particularly in the medical domain, a
specific list of symptoms can be labeled as one of various different
conditions. Some of these conditions may be more prevalent than others by
several orders of magnitude. Here we present a novel unsupervised domain
adaptation scheme for such datasets. The scheme, based on a specific type of
Quantification, is designed to work under both label and conditional shifts. It
is demonstrated on datasets generated from electronic health records and
provides high quality results for both Quantification and Domain Adaptation in
very challenging scenarios. Potential benefits of using this scheme in the
current COVID-19 outbreak, for estimation of prevalence and probability of
infection are discussed.
In many machine learning domains, datasets are characterized by highly
imbalanced and overlapping classes. Particularly in the medical domain, a
specific list of symptoms can be labeled as one of various different
conditions. Some of these conditions may be more prevalent than others by
several orders of magnitude. Here we present a novel unsupervised domain
adaptation scheme for such datasets. The scheme, based on a specific type of
Quantification, is designed to work under both label and conditional shifts. It
is demonstrated on datasets generated from electronic health records and
provides high quality results for both Quantification and Domain Adaptation in
very challenging scenarios. Potential benefits of using this scheme in the
current COVID-19 outbreak, for estimation of prevalence and probability of
infection are discussed.
Domain Adaptation in Highly Imbalanced and Overlapping Datasets
http://arxiv.org/abs/2005.03585v2