David Charte

Publication Activity (10 Years)

Years Active: 2015-2022
Publications (10 Years): 22

Top Topics

Multilabel Classification

Chest Radiographs

Top Venues

Publications

David Charte, Francisco Charte, Francisco Herrera
Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions. IEEE Trans. Pattern Anal. Mach. Intell. 44 (12) (2022)
Julián Luengo, Raúl Moreno, Iván Sevillano-García, David Charte, Adrián Peláez-Vegas, Marta Fernández-Moreno, Pablo Mesejo, Francisco Herrera
A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges. Inf. Fusion 78 (2022)
David Charte, Francisco Charte, Francisco Herrera
Reducing Data Complexity using Autoencoders with Class-informed Loss Functions. CoRR (2021)
José Daniel Pascual-Triana, David Charte, Marta Andrés Arroyo, Alberto Fernández, Francisco Herrera
Revisiting data complexity metrics based on morphology for overlap and imbalance: snapshot, new overlap number of balls metrics and singular problems prospect. Knowl. Inf. Syst. 63 (7) (2021)
David Charte, Iván Sevillano-García, María Jesús Lucena-González, José Luis Martín-Rodríguez, Francisco Charte, Francisco Herrera
Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study. HAIS (2021)
Siham Tabik, Anabel Gómez-Ríos, J. L. Martín-Rodríguez, I. Sevillano-García, Manuel Rey-Area, David Charte, Emilio Guirado, Juan-Luis Suárez, Julián Luengo, M. A. Valero-González, P. García-Villanova, E. Olmedo-Sánchez, Francisco Herrera
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images. CoRR (2020)
José Daniel Pascual-Triana, David Charte, Marta Andrés Arroyo, Alberto Fernández, Francisco Herrera
Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect. CoRR (2020)
David Charte, Francisco Charte, María José del Jesus, Francisco Herrera
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges. CoRR (2020)
Juan Manuel Górriz, Javier Ramírez, Andrés Ortiz, Francisco Jesús Martínez-Murcia, Fermín Segovia, John Suckling, Matthew Leming, Yu-Dong Zhang, José Ramón Álvarez Sánchez, Guido Bologna, María Paula Bonomini, Fernando E. Casado, David Charte, Francisco Charte, Ricardo Contreras, Alfredo Cuesta-Infante, Richard J. Duro, Antonio Fernández-Caballero, José Manuel Ferrández
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing 410 (2020)
David Charte, Francisco Charte, María José del Jesus, Francisco Herrera
A Showcase of the Use of Autoencoders in Feature Learning Applications. CoRR (2020)
Siham Tabik, Anabel Gómez-Ríos, J. L. Martín-Rodríguez, I. Sevillano-García, Manuel Rey-Area, David Charte, Emilio Guirado, Juan-Luis Suárez, Julián Luengo, M. A. Valero-González, P. García-Villanova, E. Olmedo-Sánchez, Francisco Herrera
COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images. IEEE J. Biomed. Health Informatics 24 (12) (2020)
David Charte, Francisco Charte, María José del Jesus, Francisco Herrera
An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges. Neurocomputing 404 (2020)
David Charte, Francisco Charte, Salvador García, Francisco Herrera
A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations. Prog. Artif. Intell. 8 (1) (2019)
David Charte, Francisco Charte, María José del Jesus, Francisco Herrera
A Showcase of the Use of Autoencoders in Feature Learning Applications. IWINAC (2) (2019)
David Charte, Francisco Herrera, Francisco Charte
Ruta: Implementations of neural autoencoders in R. Knowl. Based Syst. 174 (2019)
David Charte, Francisco Charte, Salvador García, Francisco Herrera
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods. CoRR (2018)
David Charte, Francisco Charte, Salvador García, María José del Jesus, Francisco Herrera
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Inf. Fusion 44 (2018)
Francisco Charte, Antonio J. Rivera, David Charte, María José del Jesus, Francisco Herrera
Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository. Neurocomputing 289 (2018)
Francisco Charte, Antonio J. Rivera, David Charte, María José del Jesus, Francisco Herrera
Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository. CoRR (2018)
David Charte, Francisco Charte, Salvador García, María José del Jesus, Francisco Herrera
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. CoRR (2018)
Francisco Charte, David Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
R Ultimate Multilabel Dataset Repository. HAIS (2016)
Francisco Charte, David Charte
Working with Multilabel Datasets in R: The mldr Package. R J. 7 (2) (2015)