| Random balance: ensembles of variable priors classifiers for imbalanced data JF Díez-Pastor, JJ Rodríguez, C García-Osorio, LI Kuncheva Knowledge-Based Systems 85, 96-111, 2015 | 117 | 2015 |
| Diversity techniques improve the performance of the best imbalance learning ensembles JF Díez-Pastor, JJ Rodríguez, CI García-Osorio, LI Kuncheva Information Sciences 325, 98-117, 2015 | 91 | 2015 |
| Nonlinear boosting projections for ensemble construction NÃĄ GarcÃa-Pedrajas, CÃĐ GarcÃa-Osorio, C Fyfe Journal of Machine Learning Research 8 (Jan), 1-33, 2007 | 90 | 2007 |
| 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 | 80 | 2010 |
| Random feature weights for decision tree ensemble construction J Maudes, JJ Rodríguez, C García-Osorio, N García-Pedrajas Information Fusion 13 (1), 20-30, 2012 | 40 | 2012 |
| Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations A Bustillo, JF Díez-Pastor, G Quintana, C García-Osorio The International Journal of Advanced Manufacturing Technology 57 (5-8), 521, 2011 | 36 | 2011 |
| Instance selection of linear complexity for big data Á Arnaiz-González, JF Díez-Pastor, JJ Rodríguez, C García-Osorio Knowledge-Based Systems 107, 83-95, 2016 | 34 | 2016 |
| Visualization of high-dimensional data via orthogonal curves C Garcıa-Osorio, C Fyfe Journal of Universal Computer Science 11 (11), 1806-1819, 2005 | 34 | 2005 |
| Supervised subspace projections for constructing ensembles of classifiers N García-Pedrajas, J Maudes-Raedo, C García-Osorio, ... Information Sciences 193, 1-21, 2012 | 31 | 2012 |
| Forests of nested dichotomies JJ Rodríguez, C García-Osorio, J Maudes Pattern Recognition Letters 31 (2), 125-132, 2010 | 31 | 2010 |
| Disturbing neighbors diversity for decision forests J Maudes, JJ Rodríguez, C García-Osorio Applications of supervised and unsupervised ensemble methods, 113-133, 2009 | 27 | 2009 |
| Fusion of instance selection methods in regression tasks Á Arnaiz-González, M Blachnik, M Kordos, C García-Osorio Information Fusion 30, 69-79, 2016 | 23 | 2016 |
| License plate number recognition-new heuristics and a comparative study of classifiers C Garcia-Osorio, JF Diez-Pastor, JJ Rodríguez, J Maudes Proceedings of the Fifth International Conference on Informatics in Control …, 2008 | 23* | 2008 |
| Constructing ensembles of classifiers using supervised projection methods based on misclassified instances N García-Pedrajas, C García-Osorio Expert Systems with Applications 38 (1), 343-359, 2011 | 22 | 2011 |
| How do B-Learning and learning patterns influence learning outcomes? MC Sáiz Manzanares, R Marticorena Sánchez, CI García Osorio, ... Frontiers in psychology 8, 745, 2017 | 19 | 2017 |
| Rotation Forests for regression C Pardo, JF Diez-Pastor, C García-Osorio, JJ Rodríguez Applied Mathematics and Computation 219 (19), 9914-9924, 2013 | 18 | 2013 |
| Random projections for linear SVM ensembles J Maudes, JJ Rodríguez, C García-Osorio, C Pardo Applied Intelligence 34 (3), 347-359, 2011 | 17 | 2011 |
| Instance selection for regression by discretization Á Arnaiz-González, JF Díez-Pastor, JJ Rodríguez, CI García-Osorio Expert Systems with Applications 54, 340-350, 2016 | 15 | 2016 |
| A tool for teaching LL and LR parsing algorithms C García-Osorio, C Gómez-Palacios, N García-Pedrajas ACM SIGCSE Bulletin 40 (3), 317-317, 2008 | 15* | 2008 |
| Instance selection for regression: Adapting DROP Á Arnaiz-González, JF Díez-Pastor, JJ Rodríguez, C García-Osorio Neurocomputing 201, 66-81, 2016 | 14 | 2016 |
Juan J. RodríguezUniversidad de BurgosVerified email at ubu.es
