José A. Sáez
José A. Sáez
Department of Computer Science and Automatic, University of Salamanca
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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
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
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
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
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
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification
JA SáEz, JN Luengo, F Herrera
Pattern Recognition 46 (1), 355-364, 2013
Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness
JA Sáez, M Galar, JN Luengo, F Herrera
Information Sciences 247, 1-20, 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
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
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
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
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
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
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
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
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
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
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
Tackling label noise with multi-class decomposition using fuzzy one-class support vector machines
B Krawczyk, JA Sáez, M Woźniak
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 915-922, 2016
SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R
PJ Villacorta, JA Sáez
The R Journal, 2015
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