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Journal Articles
- 2024
[j22]Juan Martín
, José A. Sáez, Emilio Corchado:
Tackling the problem of noisy IoT sensor data in smart agriculture: Regression noise filters for enhanced evapotranspiration prediction. Expert Syst. Appl. 237(Part B): 121608 (2024)- 2022
- [j21]José A. Sáez, Emilio Corchado:
ANCES: A novel method to repair attribute noise in classification problems. Pattern Recognit. 121: 108198 (2022) - 2021
- [j20]Juan Martín, José A. Sáez, Emilio Corchado:
On the Regressand Noise Problem: Model Robustness and Synergy With Regression-Adapted Noise Filters. IEEE Access 9: 145800-145816 (2021) - [j19]Juan Martín, José A. Sáez, Emilio Corchado:
On the suitability of stacking-based ensembles in smart agriculture for evapotranspiration prediction. Appl. Soft Comput. 108: 107509 (2021) - 2019
- [j18]José A. Sáez, Emilio Corchado:
A Meta-Learning Recommendation System for Characterizing Unsupervised Problems: On Using Quality Indices to Describe Data Conformations. IEEE Access 7: 63247-63263 (2019) - [j17]José A. Sáez, Mikel Galar, Bartosz Krawczyk:
Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy. IEEE Access 7: 83396-83411 (2019) - [j16]José A. Sáez, Emilio Corchado:
KSUFS: A Novel Unsupervised Feature Selection Method Based on Statistical Tests for Standard and Big Data Problems. IEEE Access 7: 99754-99770 (2019) - 2016
- [j15]José A. Sáez, Bartosz Krawczyk, Michal Wozniak:
On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods. Appl. Artif. Intell. 30(6): 590-609 (2016) - [j14]José A. Sáez, Julián Luengo, Francisco Herrera:
Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure. Neurocomputing 176: 26-35 (2016) - [j13]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control. Inf. Fusion 27: 19-32 (2016) - [j12]José A. Sáez, Bartosz Krawczyk, Michal Wozniak:
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets. Pattern Recognit. 57: 164-178 (2016) - 2015
- [j11]José A. Sáez, Julián Luengo, Jerzy Stefanowski, Francisco Herrera:
SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inf. Sci. 291: 184-203 (2015) - [j10]Luís Paulo F. Garcia, José A. Sáez, Julián Luengo, Ana Carolina Lorena, André C. P. L. F. de Carvalho, Francisco Herrera:
Using the One-vs-One decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. Knowl. Based Syst. 90: 153-164 (2015) - [j9]Pablo J. Villacorta, José A. Sáez:
SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R. R J. 7(2): 89 (2015) - 2014
- [j8]Isaac Triguero, José A. Sáez, Julián Luengo, Salvador García, Francisco Herrera:
On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification. Neurocomputing 132: 30-41 (2014) - [j7]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition. Knowl. Inf. Syst. 38(1): 179-206 (2014) - [j6]José A. Sáez, Joaquín Derrac, Julián Luengo, Francisco Herrera:
Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers. Pattern Recognit. 47(12): 3941-3948 (2014) - 2013
- [j5]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness. Inf. Sci. 247: 1-20 (2013) - [j4]José A. Sáez, Julián Luengo, Francisco Herrera:
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognit. 46(1): 355-364 (2013) - [j3]Salvador García, Julián Luengo, José Antonio Sáez, Victoria López, Francisco Herrera:
A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning. IEEE Trans. Knowl. Data Eng. 25(4): 734-750 (2013) - 2012
- [j2]Julián Luengo, José A. Sáez, Francisco Herrera:
Missing data imputation for fuzzy rule-based classification systems. Soft Comput. 16(5): 863-881 (2012) - [j1]Jose G. Moreno-Torres, José A. Sáez, Francisco Herrera:
Study on the Impact of Partition-Induced Dataset Shift on k -Fold Cross-Validation. IEEE Trans. Neural Networks Learn. Syst. 23(8): 1304-1312 (2012)
Conference and Workshop Papers
- 2019
- [c9]José A. Sáez, Pablo J. Villacorta, Emilio Corchado:
Dataset Weighting via Intrinsic Data Characteristics for Pairwise Statistical Comparisons in Classification. HAIS 2019: 61-72 - 2018
- [c8]José A. Sáez, Héctor Quintián, Bartosz Krawczyk, Michal Wozniak, Emilio Corchado:
Multi-class Imbalanced Data Oversampling for Vertebral Column Pathologies Classification. HAIS 2018: 131-142 - 2016
- [c7]Bartosz Krawczyk, José A. Sáez, Michal Wozniak:
Tackling label noise with multi-class decomposition using fuzzy one-class support vector machines. FUZZ-IEEE 2016: 915-922 - 2014
- [c6]José A. Sáez, Joaquín Derrac, Julián Luengo, Francisco Herrera:
Improving the Behavior of the Nearest Neighbor Classifier against Noisy Data with Feature Weighting Schemes. HAIS 2014: 597-606 - [c5]José A. Sáez, Julián Luengo, Jerzy Stefanowski, Francisco Herrera:
Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering. IDEAL 2014: 61-68 - 2013
- [c4]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
An Experimental Case of Study on the Behavior of Multiple Classifier Systems with Class Noise Datasets. HAIS 2013: 568-577 - 2012
- [c3]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
A First Study on Decomposition Strategies with Data with Class Noise Using Decision Trees. HAIS (2) 2012: 25-35 - 2011
- [c2]José A. Sáez, Julián Luengo, Francisco Herrera:
Fuzzy Rule Based Classification Systems versus crisp robust learners trained in presence of class noise's effects: A case of study. ISDA 2011: 1229-1234 - 2010
- [c1]José A. Sáez, Julián Luengo, Francisco Herrera:
A first study on the noise impact in classes for Fuzzy Rule Based Classification Systems. ISKE 2010: 153-158
Editorship
- 2019
- [e2]Manuel Graña, José Manuel López-Guede, Oier Etxaniz, Álvaro Herrero, José Antonio Sáez, Héctor Quintián, Emilio Corchado:
International Joint Conference SOCO'18-CISIS'18-ICEUTE'18 - San Sebastián, Spain, June 6-8, 2018, Proceedings. Advances in Intelligent Systems and Computing 771, Springer 2019, ISBN 978-3-319-94119-6 [contents] - 2018
- [e1]Francisco Javier de Cos Juez, José Ramón Villar, Enrique A. de la Cal, Álvaro Herrero, Héctor Quintián, José Antonio Sáez, Emilio Corchado:
Hybrid Artificial Intelligent Systems - 13th International Conference, HAIS 2018, Oviedo, Spain, June 20-22, 2018, Proceedings. Lecture Notes in Computer Science 10870, Springer 2018, ISBN 978-3-319-92638-4 [contents]
Coauthor Index
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