A first study on the noise impact in classes for Fuzzy Rule Based Classification Systems. - José A. Sáez; Julián Luengo; Francisco Herrera (ISKE, 2010)

Fuzzy Rule Based Classification Systems versus crisp robust learners trained in presence of class noise's effects: A case of study. - José A. Sáez; Julián Luengo; Francisco Herrera (ISDA, 2011)

A First Study on Decomposition Strategies with Data with Class Noise Using Decision Trees. - José A. Sáez; Mikel Galar; Julián Luengo; Francisco Herrera (HAIS (2), 2012)

Missing data imputation for fuzzy rule-based classification systems. - Julián Luengo; José A. Sáez; Francisco Herrera (Soft Comput., 2012)

Study on the Impact of Partition-Induced Dataset Shift on k -Fold Cross-Validation. - Jose G. Moreno-Torres; José A. Sáez; Francisco Herrera (IEEE Trans. Neural Networks Learn. Syst., 2012)

An Experimental Case of Study on the Behavior of Multiple Classifier Systems with Class Noise Datasets. - José A. Sáez; Mikel Galar; Julián Luengo; Francisco Herrera (HAIS, 2013)

Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness. - José A. Sáez; Mikel Galar; Julián Luengo; Francisco Herrera (Inf. Sci., 2013)

Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. - José A. Sáez; Julián Luengo; Francisco Herrera (Pattern Recognit., 2013)

A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning. - Salvador García 001; Julián Luengo; José Antonio Sáez; Victoria López; Francisco Herrera (IEEE Trans. Knowl. Data Eng., 2013)

Improving the Behavior of the Nearest Neighbor Classifier against Noisy Data with Feature Weighting Schemes. - José A. Sáez; Joaquín Derrac; Julián Luengo; Francisco Herrera (HAIS, 2014)

Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering. - José A. Sáez; Julián Luengo; Jerzy Stefanowski; Francisco Herrera (IDEAL, 2014)

On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification. - Isaac Triguero; José A. Sáez; Julián Luengo; Salvador García 001; Francisco Herrera (Neurocomputing, 2014)

Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition. - José A. Sáez; Mikel Galar; Julián Luengo; Francisco Herrera (Knowl. Inf. Syst., 2014)

Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers. - José A. Sáez; Joaquín Derrac; Julián Luengo; Francisco Herrera (Pattern Recognit., 2014)

SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. - José A. Sáez; Julián Luengo; Jerzy Stefanowski; Francisco Herrera (Inf. Sci., 2015)

Using the One-vs-One decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. - Luís Paulo F. Garcia; José A. Sáez; Julián Luengo; Ana Carolina Lorena; André C. P. L. F. de Carvalho; Francisco Herrera (Knowl. Based Syst., 2015)

SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R. - Pablo J. Villacorta; José A. Sáez (R J., 2015)

Tackling label noise with multi-class decomposition using fuzzy one-class support vector machines. - Bartosz Krawczyk; José A. Sáez; Michal Wozniak 001 (FUZZ-IEEE, 2016)

On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods. - José A. Sáez; Bartosz Krawczyk; Michal Wozniak 001 (Appl. Artif. Intell., 2016)

Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure. - José A. Sáez; Julián Luengo; Francisco Herrera (Neurocomputing, 2016)

INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control. - José A. Sáez; Mikel Galar; Julián Luengo; Francisco Herrera (Inf. Fusion, 2016)

Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets. - José A. Sáez; Bartosz Krawczyk; Michal Wozniak 001 (Pattern Recognit., 2016)

Multi-class Imbalanced Data Oversampling for Vertebral Column Pathologies Classification. - José A. Sáez; Héctor Quintián; Bartosz Krawczyk; Michal Wozniak 001; Emilio Corchado (HAIS, 2018)

Dataset Weighting via Intrinsic Data Characteristics for Pairwise Statistical Comparisons in Classification. - José A. Sáez; Pablo J. Villacorta; Emilio Corchado (HAIS, 2019)

A Meta-Learning Recommendation System for Characterizing Unsupervised Problems: On Using Quality Indices to Describe Data Conformations. - José A. Sáez; Emilio Corchado (IEEE Access, 2019)

KSUFS: A Novel Unsupervised Feature Selection Method Based on Statistical Tests for Standard and Big Data Problems. - José A. Sáez; Emilio Corchado (IEEE Access, 2019)

Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy. - José A. Sáez; Mikel Galar; Bartosz Krawczyk (IEEE Access, 2019)

On the Regressand Noise Problem: Model Robustness and Synergy With Regression-Adapted Noise Filters. - Juan Martín; José A. Sáez; Emilio Corchado (IEEE Access, 2021)

On the suitability of stacking-based ensembles in smart agriculture for evapotranspiration prediction. - Juan Martín; José A. Sáez; Emilio Corchado (Appl. Soft Comput., 2021)

ANCES: A novel method to repair attribute noise in classification problems. - José A. Sáez; Emilio Corchado (Pattern Recognit., 2022)

Compact Class-Conditional Attribute Category Clustering: Amino Acid Grouping for Enhanced HIV-1 Protease Cleavage Classification. - José A. Sáez; José Fernando Vera (IEEE ACM Trans. Comput. Biol. Bioinform., 2024)

Tackling the problem of noisy IoT sensor data in smart agriculture: Regression noise filters for enhanced evapotranspiration prediction. - Juan Martín; José A. Sáez; Emilio Corchado (Expert Syst. Appl., 2024)

When Colorimetry Meets the Cloud: IoT-Enabled Health Monitoring at Home via Highly-Specific Gas Sensing. - Ismael Benito-Altamirano; Omar Romera-Aller; Miquel Alfaras; Zouhair Haddi; Anaïs Espinoso; Xavier Llauradó; José A. Sáez; Cristian Fàbrega Gallego (AINA (3), 2025)