José A. Sáez
José A. Sáez
Dept. of Statistics and Operational Research, University of Granada
Verified email at ugr.es
Title
Cited by
Cited by
Year
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
4752012
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
2942015
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
2472012
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
1442016
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
1192014
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
972013
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
922013
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
762014
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
682016
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
532016
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
362012
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
342014
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
272015
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
242016
Managing borderline and noisy examples in imbalanced classification by combining SMOTE with ensemble filtering
JA Sáez, J Luengo, J Stefanowski, F Herrera
International Conference on Intelligent Data Engineering and Automated …, 2014
202014
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
182019
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
162011
A first study on decomposition strategies with data with class noise using decision trees
JA Sáez, M Galar, J Luengo, F Herrera
International Conference on Hybrid Artificial Intelligence Systems, 25-35, 2012
112012
A first study on the noise impact in classes for fuzzy rule based classification systems
JA Sáez, J Luengo, F Herrera
Intelligent Systems and Knowledge Engineering (ISKE), 2010 International …, 2010
92010
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
62019
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Articles 1–20