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PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force-displacement curves in T-stub steel connections.

J. Divasón, J. Fernández-Ceniceros, A. Sanz-García, A. Pernía-Espinoza, and F. de Pisón. Neurocomputing, (2023)

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Francisco Javier Martínez Concha

Francisco Javier Gonzalez Martinez

Francisco Javier Martinez Torres

Antonio Javier González Martínez

Gerardo Francisco Ramírez-Martínez

Other publications of authors with the same name

Characterization of subcortical structures during deep brain stimulation utilizing support vector machines.P. Guillén, F. de Pisón, R. Sanchez-Arias, M. Argáez, and L. Velázquez. EMBC, page 7949-7952. IEEE, (2011)Robustness Analysis of a Methodology to Detect Biases, Inconsistencies and Discrepancies in the Evaluation Process.J. Divasón, F. de Pisón, A. Romero, and E. de Cabezón. CISIS-ICEUTE, volume 748 of Lecture Notes in Networks and Systems, page 329-339. Springer, (2023)GAparsimony: An R Package for Searching Parsimonious Models by Combining Hyperparameter Optimization and Feature Selection.F. de Pisón, R. Gonzalez-Sendino, J. Ferreiro-Cabello, E. Fraile-Garcia, and A. Pernía-Espinoza. HAIS, volume 10870 of Lecture Notes in Computer Science, page 62-73. Springer, (2018)Hybrid Methodology Based on Bayesian Optimization and GA-PARSIMONY for Searching Parsimony Models by Combining Hyperparameter Optimization and Feature Selection.F. de Pisón, R. Gonzalez-Sendino, A. Aldama, J. Ferreiro-Cabello, and E. Fraile-Garcia. HAIS, volume 10334 of Lecture Notes in Computer Science, page 52-62. Springer, (2017)A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package.F. de Pisón, J. Ferreiro-Cabello, E. Fraile-Garcia, and A. Pernía-Espinoza. Neurocomputing, (2021)PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force-displacement curves in T-stub steel connections.J. Divasón, J. Fernández-Ceniceros, A. Sanz-García, A. Pernía-Espinoza, and F. de Pisón. Neurocomputing, (2023)Data mining in the process of localization and classification of subcorticals structures.P. Guillén, M. Argáez, J. Barrera, L. Velázquez, and F. de Pisón. EATIS, page 121-125. ACM, (2012)PSO-PARSIMONY: A New Methodology for Searching for Accurate and Parsimonious Models with Particle Swarm Optimization. Application for Predicting the Force-Displacement Curve in T-stub Steel Connections.J. Fernández-Ceniceros, A. Sanz-García, A. Pernía-Espinoza, and F. de Pisón. HAIS, volume 12886 of Lecture Notes in Computer Science, page 15-26. Springer, (2021)An Algorithm Based on Satellite Observations to Quality Control Ground Solar Sensors: Analysis of Spanish Meteorological Networks.R. Urraca, J. Antoñanzas-Torres, A. Sanz-García, A. Aldama, and F. de Pisón. HAIS, volume 10870 of Lecture Notes in Computer Science, page 609-621. Springer, (2018)Single and Blended Models for Day-Ahead Photovoltaic Power Forecasting.J. Antoñanzas, R. Urraca, A. Pernía-Espinoza, A. Aldama, L. Fernández-Jiménez, and F. de Pisón. HAIS, volume 10334 of Lecture Notes in Computer Science, page 427-434. Springer, (2017)

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