S. García

发表

An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons

S. García, F. Herrera, 2008 .

Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce

Francisco Herrera, Min Chen, Sergio Ramírez-Gallego, 2018, Inf. Fusion.

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

S. García, A. Hussain, J. Ser, 2020, Cognitive Computation.

Web usage mining to improve the design of an e-commerce website: OrOliveSur.com

M. J. del Jesus, C. J. Carmona, S. Ramírez-Gallego, 2012, Expert Syst. Appl..

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..

Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study

Francisco Herrera, José Ramón Cano, Salvador García, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Evolutionary Fuzzy Rule-Based Methods for Monotonic Classification

Jesús Alcalá-Fdez, Rafael Alcalá, Yusuke Nojima, 2017, IEEE Transactions on Fuzzy Systems.

Robust classification of different fingerprint copies with deep neural networks for database penetration rate reduction

J. M. Benítez, S. García, Y. Saeys, 2017 .

A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Experiments

Francisco Herrera, Salvador García, Juan Luis Suárez, 2018 .

Online entropy-based discretization for data streaming classification

S. García, F. Herrera, S. Ramírez-Gallego, 2018, Future generations computer systems.

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

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

Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization

S. García, Alejandro Rosales-Pérez, F. Herrera, 2022, IEEE Transactions on Cybernetics.

Analysis of MFGA to Extract Interesting Rules

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

PROTOTYPE GENERATION FOR NEAREST NEIGHBOR CLASSIFICATION : SURVEY OF METHODS 1 Prototype Generation for Nearest Neighbor Classification : Survey of Methods

S. García, F. Herrera, J. Derrac, 2011 .

Survey of New Approaches on Prototype Selection and Generation

S. García, F. Herrera, J. Derrac, 2011 .

A First Study on the Use of Fuzzy Rule Based Classification Systems for Problems with Imbalanced Data Sets ?

Alberto Fernández, S. García, Alberto Fernández, 2006 .

MC 2 ESVM : Multiclass Classification Based on Cooperative Evolution of Support Vector Machines

C. Coello, S. García, H. Terashima-Marín, 2018 .

A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Software pdf

Francisco Herrera, Salvador García, Juan-Luis Suárez, 2018, ArXiv.

Statistical Comparisons by Means of Non-Parametric Tests : A Case Study on Genetic Based Machine Learning

Alicia D. Benítez, S. García, F. Herrera, 2007 .

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

S. García, A. Hussain, J. Ser, 2020, Cognitive Computation.

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

Javier Del Ser, Amir Hussain, Francisco Herrera, 2020, Cognitive Computation.

Big Data Preprocessing: Enabling Smart Data

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

A Review on Evolutionary Prototype Selection

Francisco Herrera, José Ramón Cano, Salvador García, 2010, Intelligent Systems for Automated Learning and Adaptation.

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 .

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

Alejandro Barredo Arrieta, Natalia Díaz Rodríguez, S. García, 2019, Inf. Fusion.

Triguero, Isaac and González, Sergio and Moyano, Jose M. and García, Salvador and Alcalá-Fdez, Jesús and Luengo, Julian and Fernández, Alberto and del Jesús, Maria José and Sánchez, Luciano and Herrera,

S. García, Alberto Fernández, J. Alcalá-Fdez, 2017 .

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

Alejandro Barredo Arrieta, Natalia Díaz Rodríguez, S. García, 2019, Inf. Fusion.

Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy

S. García, F. Herrera, J. Cerquides, 2009, Evolutionary Computation.

A Review on Evolutionary Prototype Selection : An Empirical Study of Performance and Efficiency

S. García, J. Cano, 2015 .

Fast Case-Based Reasoning for Large-Scale Streaming Classification

Francisco Herrera, Bartosz Krawczyk, José Manuel Benítez, 2017 .

Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study

S. García, F. Herrera, I. Triguero, 2015, Knowledge and Information Systems.

Agglomerative Constrained Clustering Through Similarity and Distance Recalculation

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

Neurocomputing guest editorial for the special issue: Advances in deep and shallow machine learning approaches for handling data irregularities

Salvador García, Isaac Triguero, Swagatam Das, 2021, Neurocomputing.

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines

Francisco Charte, Salvador García, Francisco Herrera, 2018, Inf. Fusion.

On the use of convolutional neural networks for robust classification of multiple fingerprint captures

Francisco Herrera, Yvan Saeys, Salvador García, 2017, Int. J. Intell. Syst..

Fingerprint classification with a new deep neural network model: robustness for different captures of the same fingerprints pdf

Francisco Herrera, Yvan Saeys, Salvador García, 2017, ArXiv.

KEEL: a software tool to assess evolutionary algorithms for data mining problems

María José del Jesús, Jesús Alcalá-Fdez, Francisco Herrera, 2008, Soft Comput..

A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification

Francisco Herrera, Salvador García, Joaquín Derrac, 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

Monotonic classification: an overview on algorithms, performance measures and data sets

Pedro Antonio Gutiérrez, José Ramón Cano, Salvador García, 2018, Neurocomputing.

Learning from Imbalanced Data Sets

Francisco Herrera, Mikel Galar, Salvador García, 2018, Springer International Publishing.

A memetic algorithm for evolutionary prototype selection: A scaling up approach

Francisco Herrera, José Ramón Cano, Salvador García, 2008, Pattern Recognit..

MC2ESVM: Multiclass Classification Based on Cooperative Evolution of Support Vector Machines

Francisco Herrera, Carlos A. Coello Coello, Hugo Terashima-Marín, 2018, IEEE Computational Intelligence Magazine.

Applying Subgroup Discovery Based on Evolutionary Fuzzy Systems for Web Usage Mining in E-Commerce: A Case Study on OrOliveSur.com

Salvador García, Cristóbal J. Carmona, M. J. del Jesus, 2014 .

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..

Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets

Francisco Herrera, Aníbal R. Figueiras-Vidal, Salvador García, 2017, Pattern Recognit..

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 practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

Francisco Herrera, Salvador García, Joaquín Derrac, 2011, Swarm Evol. 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.

A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities

Francisco Herrera, Lior Rokach, Salvador García, 2020, Inf. Fusion.

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 empirical study on the joint impact of feature selection and data resampling on imbalance classification pdf

Paolo Soda, Gaojuan Fan, George Almpanidis, 2021, Applied Intelligence.

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

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

Evolutionary Training Set Selection to Optimize C4.5 in Imbalanced Problems

Francisco Herrera, Salvador García, S. García, 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization

Francisco Herrera, Salvador García, Daniel Molina, 2009, J. Heuristics.

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

Francisco Herrera, Nitesh V. Chawla, Salvador García, 2018, J. Artif. Intell. Res..

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..

Imbalanced Classification with Multiple Classes

Francisco Herrera, Mikel Galar, Salvador García, 2018 .

An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics

Francisco Herrera, Salvador García, Alberto Fernández, 2013, 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.

A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation

Francisco Herrera, Humberto Bustince, Mikel Galar, 2015, Inf. Sci..

A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal

Humberto Bustince, Carlos Lopez-Molina, Mikel Galar, 2015, Knowl. Based Syst..

A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models

Francisco Herrera, Humberto Bustince, Carlos Lopez-Molina, 2015, Knowl. Based Syst..

Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection

Francisco Herrera, Yvan Saeys, Salvador García, 2017, Knowl. Based Syst..

Dealing with Missing Values

Francisco Herrera, Salvador García, Julián Luengo, 2015 .

Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study

Francisco Herrera, Salvador García, Isaac Triguero, 2015, Knowledge and Information Systems.

On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection

Francisco Herrera, Chris Cornelis, Salvador García, 2012, Soft Computing.

Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection

Francisco Herrera, Chris Cornelis, Salvador García, 2012, Inf. Sci..

IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule

Francisco Herrera, Salvador García, Joaquín Derrac, 2010, Pattern Recognit..

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

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

A survey of fingerprint classification Part II

Francisco Herrera, Humberto Bustince, Carlos Lopez-Molina, 2015 .

A tutorial on distance metric learning: Mathematical foundations, algorithms, experimental analysis, prospects and challenges

Francisco Herrera, Juan Luis Su'arez-D'iaz, Salvador Garc'ia, 2018, Neurocomputing.

A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Software

S. García, F. Herrera, Juan-Luis Suárez, 2018, ArXiv.

Dimensionality Reduction for Big Data

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

A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods pdf

Francisco Charte, Francisco Herrera, Salvador García, 2018, ArXiv.

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

Francisco Charte, Francisco Herrera, Salvador García, 2018, Progress in Artificial Intelligence.

Instance reduction for one-class classification

Francisco Herrera, Salvador García, Bartosz Krawczyk, 2018, Knowledge and Information Systems.

Data discretization: taxonomy and big data challenge

Verónica Bolón-Canedo, Amparo Alonso-Betanzos, Francisco Herrera, 2016, WIREs Data Mining Knowl. Discov..

Data Preprocessing in Data Mining

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

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

S. García, J. Ser, Javier Poyatos, 2020, Cognitive Computation.

Cost-Sensitive back-propagation neural networks with binarization techniques in addressing multi-class problems and non-competent classifiers

Salvador García, Xinggang Luo, Francisco Herrera, 2017, Appl. Soft Comput..

Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme

Francisco Herrera, Salvador García, Jiafu Tang, 2017, Knowl. Based Syst..

How to design the fair experimental classifier evaluation

Salvador García, Michał Woźniak, Katarzyna Stapor, 2021, Appl. Soft Comput..

Data Intrinsic Characteristics

Francisco Herrera, Mikel Galar, Salvador García, 2018 .

Addressing imbalanced classification with instance generation techniques: IPADE-ID

Francisco Herrera, Salvador García, Cristóbal J. Carmona, 2014, Neurocomputing.

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.

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.

Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review

J. Carrasco, S. Garc'ia, M. M. Rueda, 2020, Swarm Evol. Comput..

An Evolutionary Multiobjective Model and Instance Selection for Support Vector Machines With Pareto-Based Ensembles

Francisco Herrera, Carlos A. Coello Coello, Alejandro Rosales-Pérez, 2017, IEEE Transactions on Evolutionary Computation.

Analyzing convergence performance of evolutionary algorithms: A statistical approach

Francisco Herrera, Salvador García, Ponnuthurai N. Suganthan, 2014, Inf. Sci..

A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets

María José del Jesús, Francisco Herrera, Salvador García, 2008, Fuzzy Sets Syst..

Learning from Imbalanced Data Streams

Francisco Herrera, Mikel Galar, Salvador García, 2018 .

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..

Cost-Sensitive Learning

Francisco Herrera, Mikel Galar, Salvador García, 2018 .

Integrating Instance Selection, Instance Weighting, and Feature Weighting for Nearest Neighbor Classifiers by Coevolutionary Algorithms

Francisco Herrera, Salvador García, Joaquín Derrac, 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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 .

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification

Mikel Galar, Salvador García, Dario Landa Silva, 2020, Appl. Soft Comput..

A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification

Mikel Galar, Dario Landa Silva, Isaac Triguero, 2020, 2020 IEEE Congress on Evolutionary Computation (CEC).

ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning

José Ramón Cano, Salvador García, Manuel Carpio González, 2020, Applied Sciences.

A distributed evolutionary multivariate discretizer for Big Data processing on Apache Spark

Francisco Herrera, Sergio Ramírez-Gallego, José Manuel Benítez, 2018, Swarm Evol. Comput..

Multivariate Discretization Based on Evolutionary Cut Points Selection for Classification

Francisco Herrera, Sergio Ramírez-Gallego, José Manuel Benítez, 2016, IEEE Transactions on Cybernetics.

Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms

José Ramón Cano, Salvador García, Naif R. Aljohani, 2016, Int. J. Comput. Intell. Syst..

Evolutionary fuzzy k-nearest neighbors algorithm using interval-valued fuzzy sets

Francisco Herrera, Salvador García, Francisco Chiclana, 2016, Inf. Sci..

Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: Experimental evaluation and support vector analysis

Francisco Herrera, Chris Cornelis, Salvador García, 2016, Appl. Soft Comput..

Landmark-based music recognition system optimisation using genetic algorithms

Salvador García, Salvador Gutiérrez, S. García, 2016, Multimedia Tools and Applications.

A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees

Francisco Herrera, Sergio Ramírez-Gallego, José Manuel Benítez, 2015, CORES.

A Co-evolutionary Framework for Nearest Neighbor Enhancement: Combining Instance and Feature Weighting with Instance Selection

Francisco Herrera, Salvador García, Joaquín Derrac, 2012, HAIS.

Evolutionary-based selection of generalized instances for imbalanced classification

Francisco Herrera, Salvador García, Cristóbal J. Carmona, 2012, Knowl. Based Syst..

Evolutionary selection of hyperrectangles in nested generalized exemplar learning

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

IFS-CoCo in the Landscape Contest: Description and Results

Francisco Herrera, Salvador García, Joaquín Derrac, 2010, ICPR Contests.

Stratified prototype selection based on a steady-state memetic algorithm: a study of scalability

Francisco Herrera, Salvador García, Joaquín Derrac, 2010, Memetic Comput..

A Preliminary Study on the Selection of Generalized Instances for Imbalanced Classification

Francisco Herrera, Salvador García, Cristóbal J. Carmona, 2010, IEA/AIE.

A Survey on Evolutionary Instance Selection and Generation

Francisco Herrera, Salvador García, Joaquín Derrac, 2010, Int. J. Appl. Metaheuristic Comput..

Diagnose Effective Evolutionary Prototype Selection Using an Overlapping Measure

José Ramón Cano, Francisco Herrera, Ester Bernadó-Mansilla, 2009, Int. J. Pattern Recognit. Artif. Intell..

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.

Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems

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

A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection

Francisco Herrera, Salvador García, Joaquín Derrac, 2009, HAIS.

Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes

Francisco Herrera, José Ramón Cano, Salvador García, 2008, Pattern Recognit. Lett..

Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection

Francisco Herrera, José Ramón Cano, Salvador García, 2008, Expert Syst. Appl..

Incorporating Knowledge in Evolutionary Prototype Selection

Francisco Herrera, José Ramón Cano, Salvador García, 2006, IDEAL.

A Proposal of Evolutionary Prototype Selection for Class Imbalance Problems

Francisco Herrera, José Ramón Cano, Salvador García, 2006, IDEAL.

Imbalance: Oversampling algorithms for imbalanced classification in R

Francisco Herrera, Salvador García, Alberto Fernández, 2018, Knowl. Based Syst..

Introduction to KDD and Data Science

Francisco Herrera, Mikel Galar, Salvador García, 2018 .

OCAPIS: R package for Ordinal Classification and Preprocessing in Scala

Pedro Antonio Gutiérrez, Francisco Herrera, Salvador García, 2018, Progress in Artificial Intelligence.

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.

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

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

Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification

Francisco Herrera, Salvador García, Isaac Triguero, 2011, Pattern Recognit..

A preliminary study on the use of differential evolution for adjusting the position of examples in nearest neighbor classification

Francisco Herrera, Salvador García, Isaac Triguero, 2010, IEEE Congress on Evolutionary Computation.

Prototype Selection for Nearest Neighbor Classification : Survey of Methods

Francisco Herrera, José Ramón Cano, S. García, 2010 .

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.

A First Attempt on Monotonic Training Set Selection

Salvador García, J.-R. Cano, S. García, 2018, HAIS.

Training set selection for monotonic ordinal classification

José Ramón Cano, Salvador García, S. García, 2017, Data Knowl. Eng..

Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark

Sergio Ramírez-Gallego, Bartosz Krawczyk, José Manuel Benítez, 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects

Francisco Herrera, Salvador García, Joaquín Derrac, 2014, Inf. Sci..

Imbalanced Classification for Big Data

Francisco Herrera, Mikel Galar, Salvador García, 2018 .

Statistical analysis of convergence performance throughout the evolutionary search: A case study with SaDE-MMTS and Sa-EPSDE-MMTS

Francisco Herrera, Salvador García, Ponnuthurai N. Suganthan, 2013, 2013 IEEE Symposium on Differential Evolution (SDE).

Chain based sampling for monotonic imbalanced classification

Francisco Herrera, Salvador García, Sheng-Tun Li, 2019, Inf. Sci..

Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data

Francisco Herrera, Bartosz Krawczyk, Salvador García, 2016, Knowl. Based Syst..

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.

Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy

S. García, F. Herrera, 2009, Evolutionary Computation.

An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets

María José del Jesús, Francisco Herrera, Salvador García, 2007, WILF.

A Study on the Use of the Fuzzy Reasoning Method Based on the Winning Rule vs. Voting Procedure for Classification with Imbalanced Data Sets

María José del Jesús, Francisco Herrera, Salvador García, 2007, IWANN.

Data Level Preprocessing Methods

Francisco Herrera, Mikel Galar, Salvador García, 2018 .

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

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

MRPR: A MapReduce solution for prototype reduction in big data classification

Francisco Herrera, Salvador García, Jaume Bacardit, 2015, Neurocomputing.

Integrating a differential evolution feature weighting scheme into prototype generation

Francisco Herrera, Salvador García, Joaquín Derrac, 2012, Neurocomputing.

A Preliminary Study on the Use of Fuzzy Rough Set Based Feature Selection for Improving Evolutionary Instance Selection Algorithms

Francisco Herrera, Chris Cornelis, Salvador García, 2011, IWANN.

Algorithm-Level Approaches

Francisco Herrera, Mikel Galar, Salvador García, 2018 .

Data Reduction for Big Data

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

On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection

S. García, F. Herrera, C. Cornelis, 2013, Soft Comput..

A survey on data preprocessing for data stream mining: Current status and future directions

Francisco Herrera, Sergio Ramírez-Gallego, Bartosz Krawczyk, 2017, Neurocomputing.

Minutiae-based fingerprint matching decomposition: Methodology for big data frameworks

Francisco Herrera, Salvador García, Daniel Peralta, 2017, Inf. Sci..

DPASF: a flink library for streaming data preprocessing

Francisco Herrera, Salvador García, Diego García-Gil, 2018, Big Data Analytics.

Introduction

S. García, S. Ramírez-Gallego, J. Luengo, 2020, Big Data Preprocessing.

BELIEF: A distance-based redundancy-proof feature selection method for Big Data pdf

Francisco Herrera, Salvador García, Sergio Ramírez-Gallego, 2018, Inf. Sci..

Smart Data based Ensemble for Imbalanced Big Data Classification

S. García, F. Herrera, N. Xiong, 2020, ArXiv.

Principal Components Analysis Random Discretization Ensemble for Big Data

Francisco Herrera, Sergio Ramírez-Gallego, Salvador García, 2018, Knowl. Based Syst..

IPADE: Iterative Prototype Adjustment for Nearest Neighbor Classification

Francisco Herrera, Salvador García, Isaac Triguero, 2010, IEEE Transactions on Neural Networks.

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.

A Methodology guided by Decision Trees Ensemble and Smart Data for Imbalanced Big Data

S. García, N. Xiong, Diego García-Gil, 2021 .

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).

Adaptive cooperation of multi-swarm particle swarm optimizer-based hidden Markov model

Oussama Aoun, Salvador Garcia, Abdellatif El Afia, 2019, Progress in Artificial Intelligence.

Self Inertia Weight Adaptation for the Particle Swarm Optimization

Salvador García, Abdellatif El Afia, Oussama Aoun, 2018, LOPAL '18.

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 .

Decomposition-Fusion for Label Distribution Learning

José Ramón Cano, Salvador García, Isaac Triguero, 2021, Inf. Fusion.

Monotonic Random Forest with an Ensemble Pruning Mechanism based on the Degree of Monotonicity

S. García, F. Herrera, Sergio González, 2015, New generation computing.

Managing Monotonicity in Classification by a Pruned AdaBoost

Francisco Herrera, Salvador García, Sergio González, 2016, HAIS.

Current prospects on ordinal and monotonic classification

Pedro Antonio Gutiérrez, S. García, 2016, Progress in Artificial Intelligence.

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..

Dynamic ensemble selection for multi-class imbalanced datasets

Francisco Herrera, Abdulrahman H. Altalhi, Salvador García, 2018, Inf. Sci..

An Interval Valued K-Nearest Neighbors Classifier

Francisco Herrera, Salvador García, Francisco Chiclana, 2015, IFSA-EUSFLAT.

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..

On the use of convolutional neural networks for robust classification of multiple fingerprint captures

J. M. Benítez, S. García, Y. Saeys, 2017, Int. J. Intell. Syst..

A comparison on scalability for batch big data processing on Apache Spark and Apache Flink

Francisco Herrera, Sergio Ramírez-Gallego, Salvador García, 2017 .

Cooperative multi-objective evolutionary support vector machines for multiclass problems

Francisco Herrera, Carlos A. Coello Coello, Hugo Terashima-Marín, 2018, GECCO.

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

Francisco Herrera, Salvador García, Julián Luengo, 2015 .

SOUL: Scala Oversampling and Undersampling Library for imbalance classification

Salvador García, Alberto Fernández, Francisco Herrera, 2021, SoftwareX.

Software and Libraries for Imbalanced Classification

Francisco Herrera, Mikel Galar, Salvador García, 2018 .

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).

Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection

S. García, M. Lozano, J. Cano, 2008, Expert Syst. Appl..

Making Cn2-sd Subgroup Discovery Algorithm Scalable to Large Size Data Sets Using Instance Selection

S. García, F. Herrera, M. Lozano, 2022 .

Smart Data

S. García, S. Ramírez-Gallego, Francisco Herrera, 2020, Big Data Preprocessing.

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

S. García, F. Herrera, Alberto Fernández, 2017, Int. J. Comput. Intell. Syst..

Data Preparation Basic Models

Francisco Herrera, Salvador García, Julián Luengo, 2015 .

Ordinal regression with explainable distance metric learning based on ordered sequences

Francisco Herrera, Salvador García, Juan Luis Suárez, 2021, Machine Learning.