26 citations · 49 across the 4 of their papers we have counts for
4 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…
Smart Data driven Decision Trees Ensemble Methodology for Imbalanced Big Data
Diego García-Gil, Salvador García, Ning Xiong +1
Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to trad…