J. Luengo

发表

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

Francisco Herrera, Salvador García, Alberto Fernández, 2009, Soft Comput..

Genetics-Based Machine Learning for Rule Induction : Taxonomy , Experimental Study and State of the Art

S. García, F. Herrera, Alberto Fernández, 2009 .

Analysis of MFGA to Extract Interesting Rules

S. García, J. Luengo, J. Derrac, 2013 .

Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation pdf

Francisco Herrera, Bartosz Krawczyk, Siham Tabik, 2018, Expert Syst. Appl..

Big Data Preprocessing: Enabling Smart Data

Francisco Herrera, Sergio Ramírez-Gallego, Julián Luengo, 2020 .

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)

José Javier Valle-Alonso, J. Luengo, S. Damas, 2022, ArXiv.

Comparison of KEEL versus open source Data Mining tools : Knime and Weka software Complementary Material for the paper KEEL : Multi-Stage Analysis in Data Mining

José A. Sáez, S. García, F. Herrera, 2016 .

A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training

J. Luengo, Francisco Herrera, A. G'omez-R'ios, 2021, ArXiv.

On the Suitability of Fuzzy Rule-Based Classification Systems with Noisy Data

Francisco Herrera, Julián Luengo, José A. Sáez, 2012, IEEE transactions on fuzzy systems.

The influence of noise on the evolutionary fuzzy systems for subgroup discovery

C. J. Carmona, M. D. Pérez-Godoy, J. Luengo, 2016, Soft Computing.

Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise

J. Luengo, R. Prati, Francisco Herrera, 2018, Knowledge and Information Systems.

Agglomerative Constrained Clustering Through Similarity and Distance Recalculation

Salvador García, Julián Luengo, Juan-Luis Suárez, 2020, Hybrid Artificial Intelligence Systems.

A Study on the Noise Label Influence in Boosting Algorithms: AdaBoost, GBM and XGBoost

Francisco Herrera, Anabel Gómez-Ríos, Julián Luengo, 2017, HAIS.

Exclusión social y exclusión educativa como fracasos. Conceptos y líneas para su comprensión e investigación

J. Luengo, J. Taberner, Magdalena Jiménez, 2009 .

A First Approach in the Class Noise Filtering Approaches for Fuzzy Subgroup Discovery

Cristóbal J. Carmona, Julián Luengo, C. J. Carmona, 2015, SOCO.

A preliminary study on missing data imputation in evolutionary fuzzy systems of subgroup discovery

María José del Jesús, Cristóbal J. Carmona, Julián Luengo, 2012, 2012 IEEE International Conference on Fuzzy Systems.

Fast and Scalable Approaches to Accelerate the Fuzzy k-Nearest Neighbors Classifier for Big Data

Francisco Herrera, Salvador García, Julián Luengo, 2020, IEEE Transactions on Fuzzy Systems.

Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data

Francisco Herrera, Julián Luengo, Isaac Triguero, 2018, WIREs Data Mining Knowl. Discov..

A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification

Francisco Herrera, Salvador García, Julián Luengo, 2018, 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

Tutorial on practical tips of the most influential data preprocessing algorithms in data mining

Francisco Herrera, Salvador García, Julián Luengo, 2016, Knowl. Based Syst..

Enabling Smart Data: Noise filtering in Big Data classification

Francisco Herrera, Julián Luengo, Salvador García, 2017, Inf. Sci..

KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework

Jesús Alcalá-Fdez, Joaquín Derrac, Julián Luengo, 2011, J. Multiple Valued Log. Soft Comput..

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

Francisco Herrera, Salvador García, Julián Luengo, 2010, Neural Networks.

Data Sets and Proper Statistical Analysis of Data Mining Techniques

Francisco Herrera, Salvador García, Julián Luengo, 2015 .

Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study

Francisco Herrera, Salvador García, Ester Bernadó-Mansilla, 2010, IEEE Transactions on Evolutionary Computation.

An automatic extraction method of the domains of competence for learning classifiers using data complexity measures

Francisco Herrera, Julián Luengo, J. Luengo, 2013, Knowledge and Information Systems.

A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms

Francisco Herrera, Salvador García, Cristóbal J. Carmona, 2012, ICPRAM.

A Survey on Semi-Supervised Semantic Segmentation

J. Luengo, Pablo Mesejo, Adrian Pel'aez-Vegas, 2023, ArXiv.

Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

Francisco Herrera, Salvador García, Alberto Fernández, 2010, Inf. Sci..

On the choice of the best imputation methods for missing values considering three groups of classification methods

Francisco Herrera, Salvador García, Julián Luengo, 2012, Knowledge and Information Systems.

Dealing with Missing Values

Francisco Herrera, Salvador García, Julián Luengo, 2015 .

Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling

Francisco Herrera, Salvador García, Alberto Fernández, 2011, Soft Comput..

Dimensionality Reduction for Big Data

Sergio Ramírez-Gallego, Julián Luengo, Francisco Herrera, 2020 .

Data Preprocessing in Data Mining

Francisco Herrera, Salvador García, Julián Luengo, 2014, Intelligent Systems Reference Library.

Shared domains of competence of approximate learning models using measures of separability of classes

Francisco Herrera, Julián Luengo, F. Herrera, 2012, Inf. Sci..

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

Pablo Mesejo, Julián Luengo, David Charte, 2021, Inf. Fusion.

Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering

Francisco Herrera, Jerzy Stefanowski, Julián Luengo, 2014, IDEAL.

Social and educational exclusion as failures. Framework for their understanding and research

J. Luengo, J. Taberner, M. Jiménez, 2009 .

Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers

Francisco Herrera, Joaquín Derrac, Julián Luengo, 2014, Pattern Recognit..

Improving the Behavior of the Nearest Neighbor Classifier against Noisy Data with Feature Weighting Schemes

Francisco Herrera, Joaquín Derrac, Julián Luengo, 2014, HAIS.

José Ramón Cano, Julián Luengo, Salvador García, 2020, HICSS.

Label Noise Filtering Techniques to Improve Monotonic Classification pdf

José Ramón Cano, Salvador García, Julián Luengo, 2018, Neurocomputing.

CNC-NOS: Class noise cleaning by ensemble filtering and noise scoring

Francisco Herrera, Abdulrahman H. Altalhi, Saleh Alshomrani, 2018, Knowl. Based Syst..

A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning

Francisco Herrera, Salvador García, Julián Luengo, 2013, IEEE Transactions on Knowledge and Data Engineering.

Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness

Francisco Herrera, Mikel Galar, Julián Luengo, 2013, Inf. Sci..

KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining

María José del Jesús, Francisco Herrera, Jesús Alcalá-Fdez, 2017, Int. J. Comput. Intell. Syst..

Enhancing instance-level constrained clustering through differential evolution

José Ramón Cano, Salvador García, Julián Luengo, 2021, Appl. Soft Comput..

Big data preprocessing: methods and prospects

Francisco Herrera, Sergio Ramírez-Gallego, José Manuel Benítez, 2016 .

Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure

Francisco Herrera, Julián Luengo, José A. Sáez, 2016, Neurocomputing.

Evolutionary selection of hyperrectangles in nested generalized exemplar learning

Francisco Herrera, Salvador García, Cristóbal J. Carmona, 2011, Appl. Soft Comput..

A First Approach to Nearest Hyperrectangle Selection by Evolutionary Algorithms

Francisco Herrera, Salvador García, Joaquín Derrac, 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise

Francisco Herrera, Ronaldo C. Prati, Julián Luengo, 2018, Knowledge and Information Systems.

A First Study on the Use of Noise Filtering to Clean the Bags in Multi-Instance Classification

Francisco Herrera, Ronaldo C. Prati, Julián Luengo, 2018, LOPAL '18.

A First Study on the Use of Boosting for Class Noise Reparation

Francisco Herrera, Pablo Morales-Alvarez, Julián Luengo, 2016, HAIS.

INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control

Francisco Herrera, Mikel Galar, Julián Luengo, 2016, Inf. Fusion.

Using the One-vs-One decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems

Francisco Herrera, André Carlos Ponce de Leon Ferreira de Carvalho, Ana Carolina Lorena, 2015, Knowl. Based Syst..

SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering

Francisco Herrera, Jerzy Stefanowski, Julián Luengo, 2015, Inf. Sci..

Dealing with Noisy Data

Francisco Herrera, Salvador García, Julián Luengo, 2015 .

On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification

Francisco Herrera, Salvador García, Julián Luengo, 2014, Neurocomputing.

Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification

Francisco Herrera, Julián Luengo, José A. Sáez, 2013, Pattern Recognit..

Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition

José A. Sáez, F. Herrera, J. Luengo, 2012, Knowledge and Information Systems.

Fuzzy Rule Based Classification Systems versus crisp robust learners trained in presence of class noise's effects: A case of study

Francisco Herrera, Julián Luengo, José A. Sáez, 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism

José Ramón Cano, Salvador García, Alejandro Rosales-Pérez, 2021, Swarm Evol. Comput..

Coral species identification with texture or structure images using a two-level classifier based on Convolutional Neural Networks

Francisco Herrera, Siham Tabik, Anabel Gómez-Ríos, 2019, Knowl. Based Syst..

An analysis on the use of pre-processing methods in evolutionary fuzzy systems for subgroup discovery

María José del Jesús, Cristóbal J. Carmona, Julián Luengo, 2012, Expert Syst. Appl..

Big Data Software

Sergio Ramírez-Gallego, Julián Luengo, Francisco Herrera, 2020 .

Big Data Preprocessing as the Bridge between Big Data and Smart Data: BigDaPSpark and BigDaPFlink Libraries

Francisco Herrera, Julián Luengo, Salvador García, 2019, IoTBDS.

Missing data imputation for fuzzy rule-based classification systems

José A. Sáez, F. Herrera, J. Luengo, 2012, Soft Comput..

Addressing Data-Complexity for Imbalanced Data-Sets: A Preliminary Study on the Use of Preprocessing for C4.5

Francisco Herrera, Salvador García, Alberto Fernández, 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

Preprocessing methodology for time series: An industrial world application case study

Francisco Herrera, Julián Luengo, Salvador García, 2020, Inf. Sci..

Data Reduction for Big Data

Sergio Ramírez-Gallego, Julián Luengo, Francisco Herrera, 2020 .

Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms

Jacinto Carrasco, Irina Markova, Diego Garc'ia, 2021, Neurocomputing.

3SHACC: Three stages hybrid agglomerative constrained clustering

J. Luengo, J. Cano, Juan-Luis Suárez, 2021, Neurocomputing.

DILS: Constrained clustering through dual iterative local search

José Ramón Cano, Salvador García, Julián Luengo, 2020, Comput. Oper. Res..

Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism

Alejandro Rosales-Pérez, Julián Luengo, Salvador García, 2020, GECCO.

An automatic extraction method of the domains of competence for learning classifiers using data complexity measures

F. Herrera, J. Luengo, 2013, Knowledge and Information Systems.

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

Francisco Herrera, Julián Luengo, F. Herrera, 2010, Fuzzy Sets Syst..

Exact fuzzy k-nearest neighbor classification for big datasets

Francisco Herrera, Salvador García, Julián Luengo, 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

The NoiseFiltersR Package: Label Noise Preprocessing in R

Francisco Herrera, Ana Carolina Lorena, André C. P. L. F. de Carvalho, 2017, R J..

A First Approach on Big Data Missing Values Imputation

Francisco Herrera, Julián Luengo, Jesús Maillo, 2019, IoTBDS.

Imperfect Big Data

Sergio Ramírez-Gallego, Julián Luengo, Francisco Herrera, 2020 .

Imputation of Missing Values Methods ’ Description

S. García, F. Herrera, J. Luengo, 2009 .

Fusing anomaly detection with false positive mitigation methodology for predictive maintenance under multivariate time series

J. Luengo, Marta García-Bárzana, Diego García-Gil, 2023, Inf. Fusion.

A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests

Francisco Herrera, Salvador García, Julián Luengo, 2007, Expert Syst. Appl..

Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes

Francisco Herrera, Julián Luengo, F. Herrera, 2009, IWANN.

From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification

Francisco Herrera, Julián Luengo, Diego García-Gil, 2019, Int. J. Intell. Syst..

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

Francisco Herrera, Julián Luengo, F. Herrera, 2010, International Conference on Fuzzy Systems.

A first study on the noise impact in classes for Fuzzy Rule Based Classification Systems

Francisco Herrera, Jose A. Saez, José A. Sáez, 2010, 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering.

On the use of Measures of Separability of Classes to Characterise the Domains of Competence of a Fuzzy Rule Based Classification System

Francisco Herrera, Julián Luengo, F. Herrera, 2009, IFSA/EUSFLAT Conf..

Multi-step Histogram Based Outlier Scores for Unsupervised Anomaly Detection: ArcelorMittal Engineering Dataset Case of Study

J. Luengo, Marta García-Bárzana, Diego García-Gil, 2023, Neurocomputing.

A Data Mining Software Package Including Data Preparation and Reduction: KEEL

Francisco Herrera, Salvador García, Julián Luengo, 2015 .

From Big Data to Smart Data with the K-Nearest Neighbours Algorithm

Francisco Herrera, Salvador García, Julián Luengo, 2016, 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

The influence of noise on the evolutionary fuzzy systems for subgroup discovery

M. Dolores Pérez-Godoy, Cristóbal J. Carmona, Ángel Miguel García-Vico, 2016, Soft Comput..