José A. Sáez

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 14

Top Topics

Class Imbalance

Nearest Neighbor

Top Venues

Pattern Recognit.

Publications

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) (2024)
José A. Sáez, Emilio Corchado
ANCES: A novel method to repair attribute noise in classification problems. Pattern Recognit. 121 (2022)
Juan Martín, José A. Sáez, Emilio Corchado
On the Regressand Noise Problem: Model Robustness and Synergy With Regression-Adapted Noise Filters. IEEE Access 9 (2021)
Juan Martín, José A. Sáez, Emilio Corchado
On the suitability of stacking-based ensembles in smart agriculture for evapotranspiration prediction. Appl. Soft Comput. 108 (2021)
José A. Sáez, Pablo J. Villacorta, Emilio Corchado
Dataset Weighting via Intrinsic Data Characteristics for Pairwise Statistical Comparisons in Classification. HAIS (2019)
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 (2019)
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 (2019)
José A. Sáez, Mikel Galar, Bartosz Krawczyk
Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy. IEEE Access 7 (2019)
José A. Sáez, Héctor Quintián, Bartosz Krawczyk, Michal Wozniak, Emilio Corchado
Multi-class Imbalanced Data Oversampling for Vertebral Column Pathologies Classification. HAIS (2018)
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 (2016)
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 (2016)
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 (2016)
Bartosz Krawczyk, José A. Sáez, Michal Wozniak
Tackling label noise with multi-class decomposition using fuzzy one-class support vector machines. FUZZ-IEEE (2016)
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) (2016)
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 (2015)
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 (2015)
Pablo J. Villacorta, José A. Sáez
SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R. R J. 7 (2) (2015)
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) (2014)
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)
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) (2014)
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 (2014)
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)
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)
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 (2013)
José A. Sáez, Julián Luengo, Francisco Herrera
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognit. 46 (1) (2013)
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) (2012)
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)
Julián Luengo, José A. Sáez, Francisco Herrera
Missing data imputation for fuzzy rule-based classification systems. Soft Comput. 16 (5) (2012)
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)
José A. Sáez, Julián Luengo, Francisco Herrera
A first study on the noise impact in classes for Fuzzy Rule Based Classification Systems. ISKE (2010)