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2020 – today
- 2022
[j11]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: 232-253 (2022)- [j10]David Charte, Francisco Charte, Francisco Herrera:
Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9549-9560 (2022) - 2021
- [j9]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): 1961-1989 (2021) - [c3]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: 305-315 - [i8]David Charte, Francisco Charte, Francisco Herrera:
Reducing Data Complexity using Autoencoders with Class-informed Loss Functions. CoRR abs/2111.06142 (2021) - 2020
- [j8]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: 93-107 (2020) - [j7]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: 237-270 (2020) - [j6]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, Eulalia 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): 3595-3605 (2020) - [i7]David Charte, Francisco Charte, María José del Jesus, Francisco Herrera:
A Showcase of the Use of Autoencoders in Feature Learning Applications. CoRR abs/2005.04321 (2020) - [i6]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 abs/2005.10516 (2020) - [i5]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, Eulalia Olmedo-Sánchez, Francisco Herrera:
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images. CoRR abs/2006.01409 (2020) - [i4]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 abs/2007.07935 (2020)
2010 – 2019
- 2019
- [j5]David Charte, Francisco Herrera, Francisco Charte:
Ruta: Implementations of neural autoencoders in R. Knowl. Based Syst. 174: 4-8 (2019) - [j4]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): 1-14 (2019) - [c2]David Charte, Francisco Charte, María José del Jesus, Francisco Herrera:
A Showcase of the Use of Autoencoders in Feature Learning Applications. IWINAC (2) 2019: 412-421 - 2018
- [j3]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: 68-85 (2018) - [j2]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: 78-96 (2018) - [i3]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 abs/1801.01586 (2018) - [i2]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 abs/1802.03568 (2018) - [i1]David Charte, Francisco Charte, Salvador García, Francisco Herrera:
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods. CoRR abs/1811.12044 (2018) - 2016
- [c1]Francisco Charte, David Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera:
R Ultimate Multilabel Dataset Repository. HAIS 2016: 487-499 - 2015
- [j1]Francisco Charte, David Charte:
Working with Multilabel Datasets in R: The mldr Package. R J. 7(2): 149 (2015)
Coauthor Index
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