A Study on the Use of Statistical Tests for Experimentation with Neural Networks. - Julián Luengo; Salvador García 001; Francisco Herrera (IWANN, 2007)

On the use of Measures of Separability of Classes to Characterise the Domains of Competence of a Fuzzy Rule Based Classification System. - Julián Luengo; Francisco Herrera (IFSA/EUSFLAT Conf., 2009)

Implementation and Integration of Algorithms into the KEEL Data-Mining Software Tool. - Alberto Fernández 001; Julián Luengo; Joaquín Derrac; Jesús Alcalá-Fdez; Francisco Herrera (IDEAL, 2009)

A First Approach to Nearest Hyperrectangle Selection by Evolutionary Algorithms. - Salvador García 001; Joaquín Derrac; Julián Luengo; Francisco Herrera (ISDA, 2009)

Addressing Data-Complexity for Imbalanced Data-Sets: A Preliminary Study on the Use of Preprocessing for C4.5. - Julián Luengo; Alberto Fernández 001; Salvador García 001; Francisco Herrera (ISDA, 2009)

Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes. - Julián Luengo; Francisco Herrera (IWANN (1), 2009)

A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests. - Julián Luengo; Salvador García 001; Francisco Herrera (Expert Syst. Appl., 2009)

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. - Salvador García 001; Alberto Fernández 001; Julián Luengo; Francisco Herrera (Soft Comput., 2009)

An extraction method for the characterization of the Fuzzy Rule Based Classification Systems' behavior using data complexity measures: A case of study with FH-GBML. - Julián Luengo; Francisco Herrera (FUZZ-IEEE, 2010)

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

Domains of competence of fuzzy rule based classification systems with data complexity measures: A case of study using a fuzzy hybrid genetic based machine learning method. - Julián Luengo; Francisco Herrera (Fuzzy Sets Syst., 2010)

Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. - Salvador García 001; Alberto Fernández 001; Julián Luengo; Francisco Herrera (Inf. Sci., 2010)

A study on the use of imputation methods for experimentation with Radial Basis Function Network classifiers handling missing attribute values: The good synergy between RBFNs and EventCovering method. - Julián Luengo; Salvador García 001; Francisco Herrera (Neural Networks, 2010)

Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study. - Alberto Fernández 001; Salvador García 001; Julián Luengo; Ester Bernadó-Mansilla; Francisco Herrera (IEEE Trans. Evol. Comput., 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)

Evolutionary selection of hyperrectangles in nested generalized exemplar learning. - Salvador García 001; Joaquín Derrac; Julián Luengo; Cristóbal J. Carmona; Francisco Herrera (Appl. Soft Comput., 2011)

KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. - Jesús Alcalá-Fdez; Alberto Fernández 001; Julián Luengo; Joaquín Derrac; Salvador García 001 (J. Multiple Valued Log. Soft Comput., 2011)

Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling. - Julián Luengo; Alberto Fernández 001; Salvador García 001; Francisco Herrera (Soft Comput., 2011)

A preliminary study on missing data imputation in evolutionary fuzzy systems of subgroup discovery. - Cristóbal J. Carmona; Julián Luengo; Pedro González 001; María José del Jesus (FUZZ-IEEE, 2012)

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)

A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms. - Salvador García 001; Victoria López; Julián Luengo; Cristóbal J. Carmona; Francisco Herrera (ICPRAM (1), 2012)

An analysis on the use of pre-processing methods in evolutionary fuzzy systems for subgroup discovery. - Cristóbal J. Carmona; Julián Luengo; Pedro González 001; María José del Jesus (Expert Syst. Appl., 2012)

Shared domains of competence of approximate learning models using measures of separability of classes. - Julián Luengo; Francisco Herrera (Inf. Sci., 2012)

On the choice of the best imputation methods for missing values considering three groups of classification methods. - Julián Luengo; Salvador García 001; Francisco Herrera (Knowl. Inf. Syst., 2012)

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

Naive Bayes Classifier with Mixtures of Polynomials. - Julián Luengo; Rafael Rumí (ICPRAM (1), 2015)

A First Approach in the Class Noise Filtering Approaches for Fuzzy Subgroup Discovery. - Cristóbal J. Carmona; Julián Luengo (SOCO, 2015)

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)

An automatic extraction method of the domains of competence for learning classifiers using data complexity measures. - Julián Luengo; Francisco Herrera (Knowl. Inf. Syst., 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)

Data Preprocessing in Data Mining - Salvador García 001; Julián Luengo; Francisco Herrera (Springer, 2015)

A First Study on the Use of Boosting for Class Noise Reparation. - Pablo Morales-Alvarez; Julián Luengo; Francisco Herrera (HAIS, 2016)

From Big Data to Smart Data with the K-Nearest Neighbours Algorithm. - Isaac Triguero; Jesús Maillo; Julián Luengo; Salvador García 001; Francisco Herrera (iThings/GreenCom/CPSCom/SmartData, 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)

Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. - Salvador García 001; Julián Luengo; Francisco Herrera (Knowl. Based Syst., 2016)

The influence of noise on the evolutionary fuzzy systems for subgroup discovery. - Julián Luengo; Ángel Miguel García-Vico; M. Dolores Pérez-Godoy; Cristóbal J. Carmona (Soft Comput., 2016)

Exact fuzzy k-nearest neighbor classification for big datasets. - Jesús Maillo; Julián Luengo; Salvador García 001; Francisco Herrera; Isaac Triguero (FUZZ-IEEE, 2017)

A Study on the Noise Label Influence in Boosting Algorithms: AdaBoost, GBM and XGBoost. - Anabel Gómez-Ríos; Julián Luengo; Francisco Herrera (HAIS, 2017)

KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining. - Isaac Triguero; Sergio González; Jose M. Moyano; Salvador García 001; Jesús Alcalá-Fdez; Julián Luengo; Alberto Fernández 001; María José del Jesus; Luciano Sánchez; Francisco Herrera (Int. J. Comput. Intell. Syst., 2017)

The NoiseFiltersR Package: Label Noise Preprocessing in R. - Pablo Morales-Álvarez; Julián Luengo; Luís Paulo F. Garcia; Ana Carolina Lorena; André C. P. L. F. de Carvalho; Francisco Herrera (R J., 2017)

A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification. - Jesús Maillo; Julián Luengo; Salvador García 001; Francisco Herrera; Isaac Triguero (FUZZ-IEEE, 2018)

A First Study on the Use of Noise Filtering to Clean the Bags in Multi-Instance Classification. - Julián Luengo; Dánel Sánchez Tarragó; Ronaldo C. Prati; Francisco Herrera (LOPAL, 2018)

CNC-NOS: Class noise cleaning by ensemble filtering and noise scoring. - Julián Luengo; Seong-O Shim; Saleh Alshomrani; Abdulrahman H. Altalhi; Francisco Herrera (Knowl. Based Syst., 2018)

Big Data Preprocessing as the Bridge between Big Data and Smart Data: BigDaPSpark and BigDaPFlink Libraries. - Diego García-Gil; Alejandro Alcalde-Barros; Julián Luengo; Salvador García 001; Francisco Herrera (IoTBDS, 2019)

A First Approach on Big Data Missing Values Imputation. - Besay Montesdeoca; Julián Luengo; Jesús Maillo; Diego García-Gil; Salvador García 001; Francisco Herrera (IoTBDS, 2019)

Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation. - Anabel Gómez-Ríos; Siham Tabik; Julián Luengo; A. S. M. Shihavuddin; Bartosz Krawczyk; Francisco Herrera (Expert Syst. Appl., 2019)

From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification. - Diego García-Gil; Francisco Luque Sánchez; Julián Luengo; Salvador García 001; Francisco Herrera (Int. J. Intell. Syst., 2019)

Label noise filtering techniques to improve monotonic classification. - José Ramón Cano; Julián Luengo; Salvador García 001 (Neurocomputing, 2019)

Smartdata: Data preprocessing to achieve smart data in R. - Ignacio Cordón; Julián Luengo; Salvador García 001; Francisco Herrera; Francisco Charte (Neurocomputing, 2019)

Enabling Smart Data: Noise filtering in Big Data classification. - Diego García-Gil; Julián Luengo; Salvador García 001; Francisco Herrera (Inf. Sci., 2019)

Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise. - Ronaldo C. Prati; Julián Luengo; Francisco Herrera (Knowl. Inf. Syst., 2019)

Coral species identification with texture or structure images using a two-level classifier based on Convolutional Neural Networks. - Anabel Gómez-Ríos; Siham Tabik; Julián Luengo; A. S. M. Shihavuddin; Francisco Herrera (Knowl. Based Syst., 2019)

Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data. - Isaac Triguero; Diego García-Gil; Jesús Maillo; Julián Luengo; Salvador García 001; Francisco Herrera (WIREs Data Mining Knowl. Discov., 2019)

Big Data Preprocessing - Enabling Smart Data - Julián Luengo; Diego García-Gil; Sergio Ramírez-Gallego; Salvador García 001; Francisco Herrera (Springer, 2020)

Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism. - Germán González-Almagro; Alejandro Rosales-Pérez; Julián Luengo; José Ramón Cano; Salvador García 001 (GECCO, 2020)

Agglomerative Constrained Clustering Through Similarity and Distance Recalculation. - Germán González-Almagro; Juan-Luis Suárez; Julián Luengo; José Ramón Cano; Salvador García 001 (HAIS, 2020)

Similarity-based and Iterative Label Noise Filters for Monotonic Classification. - José Ramón Cano; Julián Luengo; Salvador García 001 (HICSS, 2020)

DILS: Constrained clustering through dual iterative local search. - Germán González-Almagro; Julián Luengo; José Ramón Cano; Salvador García 001 (Comput. Oper. Res., 2020)

Preprocessing methodology for time series: An industrial world application case study. - Juan Antonio Cortés-Ibáñez; Sergio González; José Javier Valle-Alonso; Julián Luengo; Salvador García 001; Francisco Herrera (Inf. Sci., 2020)

Fast and Scalable Approaches to Accelerate the Fuzzy k-Nearest Neighbors Classifier for Big Data. - Jesús Maillo; Salvador García 001; Julián Luengo; Francisco Herrera; Isaac Triguero (IEEE Trans. Fuzzy Syst., 2020)

COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images. - Siham Tabik; Anabel Gómez-Ríos; J. L. Martín-Rodríguez; I. Sevillano-García; Manuel Rey-Area; David Charte; Emilio Guirado; Juan-Luis Suárez; Julián Luengo; M. A. Valero-González; P. García-Villanova; Eulalia Olmedo-Sánchez; et al. (IEEE J. Biomed. Health Informatics, 2020)

Enhancing instance-level constrained clustering through differential evolution. - Germán González-Almagro; Julián Luengo; José Ramón Cano; Salvador García 001 (Appl. Soft Comput., 2021)

Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms. - Jacinto Carrasco; Irina Markova; David López; Ignacio Aguilera; Diego García; Marta García-Bárzana; Manuel Arias-Rodil; Julián Luengo; Francisco Herrera (CoRR, 2021)

A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training. - Anabel Gómez-Ríos; Julián Luengo; Francisco Herrera (CoRR, 2021)

Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms. - Jacinto Carrasco; David López; Ignacio Aguilera-Martos; Diego García-Gil; Irina Markova; Marta García-Bárzana; Manuel Arias-Rodil; Julián Luengo; Francisco Herrera (Neurocomputing, 2021)

Synthetic Sample Generation for Label Distribution Learning. - Manuel González; Julián Luengo; José Ramón Cano; Salvador García 001 (Inf. Sci., 2021)

Multiple instance classification: Bag noise filtering for negative instance noise cleaning. - Julián Luengo; Dánel Sánchez Tarragó; Ronaldo C. Prati; Francisco Herrera (Inf. Sci., 2021)

ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism. - Germán González-Almagro; Alejandro Rosales-Pérez; Julián Luengo; José Ramón Cano; Salvador García 001 (Swarm Evol. Comput., 2021)

TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study). - Ignacio Aguilera-Martos; Ángel Miguel García-Vico; Julián Luengo; Sergio Damas; Francisco J. Melero 001; José Javier Valle-Alonso; Francisco Herrera (CoRR, 2022)

The impact of heterogeneous distance functions on missing data imputation and classification performance. - Miriam Seoane Santos; Pedro Henriques Abreu; Alberto Fernández 001; Julián Luengo; João A. M. Santos (Eng. Appl. Artif. Intell., 2022)

3SHACC: Three stages hybrid agglomerative constrained clustering. - Germán González-Almagro; Juan-Luis Suárez; Julián Luengo; José Ramón Cano; Salvador García 001 (Neurocomputing, 2022)

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges. - Julián Luengo; Raúl Moreno; Iván Sevillano-García; David Charte; Adrián Peláez-Vegas; Marta Fernández-Moreno; Pablo Mesejo; Francisco Herrera (Inf. Fusion, 2022)

Revisiting Histogram Based Outlier Scores: Strengths and Weaknesses. - Ignacio Aguilera-Martos; Julián Luengo; Francisco Herrera (HAIS, 2023)

Optimizing LIME Explanations Using REVEL Metrics. - Iván Sevillano-García; Julián Luengo; Francisco Herrera (HAIS, 2023)

Low-Impact Feature Reduction Regularization Term: How to Improve Artificial Intelligence with Explainability. - Iván Sevillano-García; Julián Luengo; Francisco Herrera (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2023)

A Survey on Semi-Supervised Semantic Segmentation. - Adrián Peláez-Vegas; Pablo Mesejo; Julián Luengo (CoRR, 2023)

REVEL Framework to Measure Local Linear Explanations for Black-Box Models: Deep Learning Image Classification Case Study. - Iván Sevillano-García; Julián Luengo; Francisco Herrera (Int. J. Intell. Syst., 2023)

TSFEDL: A python library for time series spatio-temporal feature extraction and prediction using deep learning. - Ignacio Aguilera-Martos; Ángel Miguel García-Vico; Julián Luengo; Sergio Damas; Francisco J. Melero 001; José Javier Valle-Alonso; Francisco Herrera (Neurocomputing, 2023)

Multi-step histogram based outlier scores for unsupervised anomaly detection: ArcelorMittal engineering dataset case of study. - Ignacio Aguilera-Martos; Marta García-Bárzana; Diego García-Gil; Jacinto Carrasco; David López; Julián Luengo; Francisco Herrera (Neurocomputing, 2023)

Fusing anomaly detection with false positive mitigation methodology for predictive maintenance under multivariate time series. - David López; Ignacio Aguilera-Martos; Marta García-Bárzana; Francisco Herrera; Diego García-Gil; Julián Luengo (Inf. Fusion, 2023)

Local Attention: Enhancing the Transformer Architecture for Efficient Time Series Forecasting. - Ignacio Aguilera-Martos; Andrés Herrera-Poyatos; Julián Luengo; Francisco Herrera (IJCNN, 2024)

Combining traditional and spiking neural networks for energy-efficient detection of Eimeria parasites. - Iago Xabier Vázquez; Bahgat Ayasi; Huseyin Seker; Julián Luengo; Javier Sedano; Ángel Miguel García-Vico (Appl. Soft Comput., 2024)

SHIELD: A regularization technique for eXplainable Artificial Intelligence. - Iván Sevillano-García; Julián Luengo; Francisco Herrera (CoRR, 2024)

Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting. - Ignacio Aguilera-Martos; Andrés Herrera-Poyatos; Julián Luengo; Francisco Herrera (CoRR, 2024)

Developing Big Data anomaly dynamic and static detection algorithms: AnomalyDSD spark package. - Diego García-Gil; David López; Daniel Argüelles-Martino; Jacinto Carrasco; Ignacio Aguilera-Martos; Julián Luengo; Francisco Herrera (Inf. Sci., 2025)

STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability. - Iván Sevillano-García; Julián Luengo; Francisco Herrera (CoRR, 2025)