269 citations · 460 across the 8 of their papers we have counts for
8 papers
Reducing Data Complexity using Autoencoders with Class-informed Loss Functions
David Charte, Francisco Charte, Francisco Herrera
Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to m…
Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect
José Daniel Pascual-Triana, David Charte, Marta Andrés Arroyo +2
Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class pre…
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images
S. Tabik, A. Gómez-Ríos, J. L. Martín-Rodríguez +10
Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT…
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges
David Charte, Francisco Charte, María J. del Jesus +1
In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate w…
A Showcase of the Use of Autoencoders in Feature Learning Applications
David Charte, Francisco Charte, María J. del Jesus +1
Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding spec…
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods
David Charte, Francisco Charte, Salvador García +1
Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdiv…
Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository
Francisco Charte, Antonio J. Rivera, David Charte +2
New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always…
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
David Charte, Francisco Charte, Salvador García +2
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these…