| Democratic instance selection: a linear complexity instance selection algorithm based on classifier ensemble concepts C García-Osorio, A de Haro-García, N García-Pedrajas Artificial Intelligence 174 (5-6), 410-441, 2010 | 104 | 2010 |
| OligoIS: Scalable instance selection for class-imbalanced data sets N García-Pedrajas, J Perez-Rodriguez, A de Haro-García IEEE Transactions on Cybernetics 43 (1), 332-346, 2012 | 67 | 2012 |
| A divide-and-conquer recursive approach for scaling up instance selection algorithms A de Haro-García, N García-Pedrajas Data Mining and Knowledge Discovery 18, 392-418, 2009 | 63 | 2009 |
| A scalable approach to simultaneous evolutionary instance and feature selection NS GarcíA-Pedrajas, A De Haro-GarcíA, J PéRez-RodríGuez Information Sciences 228, 150-174, 2013 | 60 | 2013 |
| Boosting instance selection algorithms N García-Pedrajas, A de Haro-García Knowledge-Based Systems 67, 342-360, 2014 | 53 | 2014 |
| Scaling up data mining algorithms: review and taxonomy N García-Pedrajas, A de Haro-García Progress in Artificial Intelligence 1, 71-87, 2012 | 42 | 2012 |
| A scalable memetic algorithm for simultaneous instance and feature selection N García-Pedrajas, A de Haro-García, J Pérez-Rodríguez Evolutionary Computation 22 (1), 1-45, 2014 | 39 | 2014 |
| Instance selection based on boosting for instance-based learners A de Haro-García, G Cerruela-García, N García-Pedrajas Pattern Recognition 96, 106959, 2019 | 30 | 2019 |
| Ensembles of feature selectors for dealing with class-imbalanced datasets: A proposal and comparative study A de Haro-García, G Cerruela-García, N García-Pedrajas Information Sciences 540, 89-116, 2020 | 23 | 2020 |
| A general framework for boosting feature subset selection algorithms J Pérez-Rodríguez, A de Haro-Garcia, JAR del Castillo, ... Information Fusion 44, 147-175, 2018 | 21 | 2018 |
| Large scale instance selection by means of federal instance selection A de Haro-García, N García-Pedrajas, JAR del Castillo Data & Knowledge Engineering 75, 58-77, 2012 | 20 | 2012 |
| Combining three strategies for evolutionary instance selection for instance-based learning A de Haro-García, J Pérez-Rodríguez, N García-Pedrajas Swarm and Evolutionary Computation 42, 160-172, 2018 | 18 | 2018 |
| Filter feature selectors in the development of binary QSAR models G Cerruela García, J Pérez-Parras Toledano, A de Haro García, ... SAR and QSAR in Environmental Research 30 (5), 313-345, 2019 | 14 | 2019 |
| Scaling data mining algorithms. Application to instance and feature selection A Haro García Granada: Universidad de Granada, 2012 | 10 | 2012 |
| A scalable method for instance selection for class-imbalance datasets A De Haro-garcia, N García-Pedrajas 2011 11th International Conference on Intelligent Systems Design and …, 2011 | 10 | 2011 |
| Improving the combination of results in the ensembles of prototype selectors G Cerruela-García, A de Haro-García, JPP Toledano, N García-Pedrajas Neural Networks 118, 175-191, 2019 | 9 | 2019 |
| Grab’em: A novel graph-based method for combining feature subset selectors A de Haro-García, JPP Toledano, G Cerruela-García, N García-Pedrajas IEEE Transactions on Cybernetics 52 (5), 2942-2954, 2020 | 7 | 2020 |
| Floating search methodology for combining classification models for site recognition in DNA sequences J Pérez-Rodríguez, A de Haro-García, N García-Pedrajas IEEE/ACM Transactions on Computational Biology and Bioinformatics 18 (6 …, 2020 | 4 | 2020 |
| Instance selection for class imbalanced problems by means of selecting instances more than once J Pérez-Rodríguez, A de Haro-García, N García-Pedrajas Conference of the Spanish Association for Artificial Intelligence, 104-113, 2011 | 4 | 2011 |
| Output Coding Methods: Review and Experimental Comparison N García-Pedrajas, A de Haro García Pattern Recognition Techniques, Technology and Applications, 327, 2008 | 4 | 2008 |
