Large Scale Instance Selection by Means of a Parallel Algorithm. - Aida de Haro-García; Juan Antonio Romero del Castillo; Nicolás García-Pedrajas (IDEAL, 2010)

A cooperative coevolutionary algorithm for instance selection for instance-based learning. - Nicolás García-Pedrajas; Juan Antonio Romero del Castillo; Domingo Ortiz-Boyer (Mach. Learn., 2010)

Large scale instance selection by means of federal instance selection. - Aida de Haro-García; Nicolás García-Pedrajas; Juan Antonio Romero del Castillo (Data Knowl. Eng., 2012)

A Proposal for Local k Values for k-Nearest Neighbor Rule. - Nicolás García-Pedrajas; Juan Antonio Romero del Castillo; Gonzalo Cerruela García (IEEE Trans. Neural Networks Learn. Syst., 2017)

A general framework for boosting feature subset selection algorithms. - Javier Pérez-Rodríguez; Aida de Haro-García; Juan Antonio Romero del Castillo; Nicolás García-Pedrajas (Inf. Fusion, 2018)

Instance selection for multi-label learning based on a scalable evolutionary algorithm. - Juan Antonio Romero del Castillo; Domingo Ortiz-Boyer; Nicolás García-Pedrajas (ICDM (Workshops), 2021)

SI(FS)2: Fast simultaneous instance and feature selection for datasets with many features. - Nicolás García-Pedrajas; Juan Antonio Romero del Castillo; Gonzalo Cerruela García (Pattern Recognit., 2021)

Local-based k values for multi-label k-nearest neighbors rule. - Juan Antonio Romero del Castillo; Manuel Mendoza-Hurtado; Domingo Ortiz-Boyer; Nicolás García-Pedrajas (Eng. Appl. Artif. Intell., 2022)

PARIS: Partial instance and training set selection. A new scalable approach to multi-label classification. - Nicolás García-Pedrajas; José Manuel Cuevas-Muñoz; Juan Antonio Romero del Castillo; Aida de Haro-García (Inf. Fusion, 2023)

SAMPLID: A New Supervised Approach for Meaningful Place Identification Using Call Detail Records as an Alternative to Classical Unsupervised Clustering Techniques. - Manuel Mendoza-Hurtado; Juan Antonio Romero del Castillo; Domingo Ortiz-Boyer (ISPRS Int. J. Geo Inf., 2024)

Label repopulation through evolutionary computation for addressing challenging multilabel problems. - Nicolás García-Pedrajas; Juan Antonio Romero del Castillo; Aida de Haro-García (Appl. Soft Comput., 2025)