2 code implementations • 2 Jun 2020 • S. Tabik, A. Gómez-Ríos, J. L. Martín-Rodríguez, I. Sevillano-García, M. Rey-Area, D. Charte, E. Guirado, J. L. Suárez, J. Luengo, M. A. Valero-González, P. García-Villanova, E. Olmedo-Sánchez, F. Herrera
Our approach reaches good and stable results with an accuracy of $97. 72\% \pm 0. 95 \%$, $86. 90\% \pm 3. 20\%$, $61. 80\% \pm 5. 49\%$ in severe, moderate and mild COVID-19 severity levels (Paper accepted for publication in Journal of Biomedical and Health Informatics).
no code implementations • 30 Jan 2020 • S. Tabik, R. F. Alvear-Sandoval, M. M. Ruiz, J. L. Sancho-Gómez, A. R. Figueiras-Vidal, F. Herrera
This paper is three-fold: 1) It provides an overview of the most popular ensemble methods, 2) analyzes several fusion schemes using MNIST as guiding thread and 3) introduces MNIST-NET10, a complex heterogeneous fusion architecture based on a degree of certainty aggregation approach; it combines two heterogeneous schemes from the perspective of data, model and fusion strategy.
