A divide-and-conquer recursive approach for scaling up instance selection algorithms. - Aida de Haro-García; Nicolás García-Pedrajas (Data Min. Knowl. Discov., 2009)

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

Scaling Up Feature Selection by Means of Democratization. - Aida de Haro-García; Nicolás García-Pedrajas (IEA/AIE (2), 2010)

Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts. - César Ignacio García-Osorio; Aida de Haro-García; Nicolás García-Pedrajas (Artif. Intell., 2010)

A Comparison of Two Strategies for Scaling Up Instance Selection in Huge Datasets. - Aida de Haro-García; Javier Pérez-Rodríguez; Nicolás García-Pedrajas (CAEPIA, 2011)

Instance Selection for Class Imbalanced Problems by Means of Selecting Instances More than Once. - Javier Pérez-Rodríguez; Aida de Haro-García; Nicolás García-Pedrajas (CAEPIA, 2011)

Feature Selection for Translation Initiation Site Recognition. - Aida de Haro-García; Javier Pérez-Rodríguez; Nicolás García-Pedrajas (IEA/AIE (2), 2011)

A scalable method for instance selection for class-imbalance datasets. - Aida de Haro-García; Nicolás García-Pedrajas (ISDA, 2011)

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)

Scaling up data mining algorithms: review and taxonomy. - Nicolás García-Pedrajas; Aida de Haro-García (Prog. Artif. Intell., 2012)

A Scalable Feature Selection Method to Improve the Analysis of Microarrays. - Aida de Haro-García; Javier Pérez-Rodríguez; Nicolás García-Pedrajas (Modern Advances in Intelligent Systems and Tools, 2012)

A scalable approach to simultaneous evolutionary instance and feature selection. - Nicolás García-Pedrajas; Aida de Haro-García; Javier Pérez-Rodríguez (Inf. Sci., 2013)

OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets. - Nicolás García-Pedrajas; Javier Pérez-Rodríguez; Aida de Haro-García (IEEE Trans. Cybern., 2013)

A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection. - Nicolás García-Pedrajas; Aida de Haro-García; Javier Pérez-Rodríguez (Evol. Comput., 2014)

Boosting instance selection algorithms. - Nicolás García-Pedrajas; Aida de Haro-García (Knowl. Based Syst., 2014)

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)

Combining three strategies for evolutionary instance selection for instance-based learning. - Aida de Haro-García; Javier Pérez-Rodríguez; Nicolás García-Pedrajas (Swarm Evol. Comput., 2018)

Improving the combination of results in the ensembles of prototype selectors. - Gonzalo Cerruela García; Aida de Haro-García; José Pérez-Parras Toledano; Nicolás García-Pedrajas (Neural Networks, 2019)

Instance selection based on boosting for instance-based learners. - Aida de Haro-García; Gonzalo Cerruela García; Nicolás García-Pedrajas (Pattern Recognit., 2019)

Ensembles of feature selectors for dealing with class-imbalanced datasets: A proposal and comparative study. - Aida de Haro-García; Gonzalo Cerruela García; Nicolás García-Pedrajas (Inf. Sci., 2020)

Influence of feature rankers in the construction of molecular activity prediction models. - Gonzalo Cerruela García; José Pérez-Parras Toledano; Aida de Haro-García; Nicolás García-Pedrajas (J. Comput. Aided Mol. Des., 2020)

Floating Search Methodology for Combining Classification Models for Site Recognition in DNA Sequences. - Javier Pérez-Rodríguez; Aida de Haro-García; Nicolás García-Pedrajas (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

Grab'Em: A Novel Graph-Based Method for Combining Feature Subset Selectors. - Aida de Haro-García; José Pérez-Parras Toledano; Gonzalo Cerruela García; Nicolás García-Pedrajas (IEEE Trans. Cybern., 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)

Evolutionary simultaneous under and oversampling of instances for dealing with class-imbalance datasets in multilabel problems. - Nicolás García-Pedrajas; José Manuel Cuevas-Muñoz; Aida de Haro-García (Appl. Soft Comput., 2024)

Extensive experimental comparison among multilabel methods focused on ranking performance. - Nicolás García-Pedrajas; José Manuel Cuevas-Muñoz; Gonzalo Cerruela García; Aida de Haro-García (Inf. Sci., 2024)

A thorough experimental comparison of multilabel methods for classification performance. - Nicolás García-Pedrajas; José Manuel Cuevas-Muñoz; Gonzalo Cerruela García; Aida de Haro-García (Pattern Recognit., 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)