Juan Luis Suárez

TL;DR: The high sensitivities achieved by most recent COVID-19 classification models are demystified, a homogeneous and balanced database that includes all levels of severity, from normal with Positive RT-PCR, Mild, Moderate to Severe is built and COVID Smart Data based Network (COVID-SDNet) methodology is proposed for improving the generalization capacity of CO VID-classification models.

TL;DR: In this article, the authors proposed COVIDGR-1.0, a homogeneous and balanced database that includes all levels of severity, from normal with Positive RT-PCR, Mild, Moderate to Severe.

TL;DR: All the algorithms studied in this paper will be evaluated with exhaustive testing in order to analyze their capabilities in standard classification problems, particularly considering dimensionality reduction and kernelization.

TL;DR: A Python package is presented that collects a set of 17 distance metric learning techniques explained in this paper, with some experiments to evaluate the performance of the different algorithms.

TL;DR: pyDML is an open-source python library that provides a wide range of distance metric learning algorithms, which can be categorized, according to their purpose, in: dimensionality reduction algorithms, algorithms to improve nearest neighbors or nearest centroids classifiers, information theory based algorithms or kernel based algorithms, among others.