| A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning S Garcia, J Luengo, JA Sáez, V Lopez, F Herrera IEEE transactions on Knowledge and Data Engineering 25 (4), 734-750, 2012 | 648 | 2012 |
| SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering JA Sáez, J Luengo, J Stefanowski, F Herrera Information Sciences 291, 184-203, 2015 | 582 | 2015 |
| Study on the Impact of Partition-Induced Dataset Shift on k-fold Cross-Validation JG Moreno-Torres, JA Sáez, F Herrera Neural Networks and Learning Systems, IEEE Transactions on 23 (8), 1304-1312, 2012 | 391 | 2012 |
| Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets JA Sáez, B Krawczyk, M Woźniak Pattern Recognition 57, 164-178, 2016 | 248 | 2016 |
| Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition JA Sáez, M Galar, J Luengo, F Herrera Knowledge and Information Systems 38 (1), 179-206, 2014 | 163 | 2014 |
| Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness JA Sáez, M Galar, J Luengo, F Herrera Information Sciences 247, 1-20, 2013 | 133 | 2013 |
| Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification JA Sáez, J Luengo, F Herrera Pattern Recognition 46 (1), 355-364, 2013 | 117 | 2013 |
| On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification I Triguero, JA Sáez, J Luengo, S García, F Herrera Neurocomputing 132, 30-41, 2014 | 108 | 2014 |
| INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control JA Sáez, M Galar, J Luengo, F Herrera Information Fusion 27, 19-32, 2016 | 97 | 2016 |
| Evaluating the classifier behavior with noisy data considering performance and robustness: The equalized loss of accuracy measure JA Sáez, J Luengo, F Herrera Neurocomputing 176, 26-35, 2016 | 79 | 2016 |
| Missing data imputation for fuzzy rule-based classification systems J Luengo, JA Sáez, F Herrera Soft Computing-A Fusion of Foundations, Methodologies and Applications, 1-19, 2012 | 51 | 2012 |
| On the influence of class noise in medical data classification: Treatment using noise filtering methods JA Sáez, B Krawczyk, M Woźniak Applied Artificial Intelligence 30 (6), 590-609, 2016 | 46 | 2016 |
| Addressing the overlapping data problem in classification using the one-vs-one decomposition strategy JA Sáez, M Galar, B Krawczyk IEEE Access 7, 83396-83411, 2019 | 45 | 2019 |
| Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers JA Sáez, J Derrac, J Luengo, F Herrera Pattern Recognition 47 (12), 3941-3948, 2014 | 42 | 2014 |
| Using the one-vs-one decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems LPF Garcia, JA Sáez, J Luengo, AC Lorena, AC de Carvalho, F Herrera Knowledge-Based Systems 90, 153-164, 2015 | 39 | 2015 |
| Managing borderline and noisy examples in imbalanced classification by combining SMOTE with ensemble filtering JA Sáez, J Luengo, J Stefanowski, F Herrera Intelligent Data Engineering and Automated Learning–IDEAL 2014: 15th …, 2014 | 30 | 2014 |
| Fuzzy rule based classification systems versus crisp robust learners trained in presence of class noise's effects: a case of study JA Sáez, J Luengo, F Herrera 2011 11th International Conference on Intelligent Systems Design and …, 2011 | 24 | 2011 |
| On the suitability of stacking-based ensembles in smart agriculture for evapotranspiration prediction J Martin, JA Saez, E Corchado Applied Soft Computing 108, 107509, 2021 | 20 | 2021 |
| ANCES: A novel method to repair attribute noise in classification problems JA Sáez, E Corchado Pattern Recognition 121, 108198, 2022 | 17 | 2022 |
| A meta-learning recommendation system for characterizing unsupervised problems: On using quality indices to describe data conformations JA Sáez, E Corchado IEEE Access 7, 63247-63263, 2019 | 17 | 2019 |
Mikel GalarAssociate Professor of Computer Science and Artificial Intelligence, Universidad Pública de Navarraبريد إلكتروني تم التحقق منه على unavarra.es
