Julián Luengo Martín - Google Académico

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Francisco HerreraProfessor Computer Science and AI, Granada Univ.; Senior Associate Researcher in DL&ML at Sherpa.aiDirección de correo verificada de decsai.ugr.es
Salvador GarcíaFull Professor of Computer Science and Artificial Intelligence. University of Granada.Dirección de correo verificada de decsai.ugr.es
Alberto FernándezAssociate Professor in Computer Science and Artificial Intelligence, University of GranadaDirección de correo verificada de decsai.ugr.es
José A. SáezDepartment of Computer Science and Automatic, University of SalamancaDirección de correo verificada de usal.es
Joaquin Derrac RusData ScientistDirección de correo verificada de gresearch.co.uk
Jesus Alcala-FdezAssociate Professor of Computer Science and Artificial Intelligence, University of GranadaDirección de correo verificada de decsai.ugr.es
Luciano SanchezProfessor of Computer Science, Oviedo UniversityDirección de correo verificada de uniovi.es
Sergio Ramírez-GallegoPhD. Student Computer Science, University of GranadaDirección de correo verificada de decsai.ugr.es
Isaac Triguero VelázquezAssociate Professor in Data Science, School of Computer Science, University of NottinghamDirección de correo verificada de nottingham.ac.uk
Diego García-GilPostdoctoral Researcher in Big Data Science, University of GranadaDirección de correo verificada de decsai.ugr.es
Mikel GalarAssociate Professor of Computer Science and Artificial Intelligence, Universidad Pública de NavarraDirección de correo verificada de unavarra.es
Victoria Lopez MoralesResearch Associate, Imperial College LondonDirección de correo verificada de imperial.ac.uk
Jesús Maillo HidalgoPh.D. Department of Computer Science and Artificial Intelligence, University of GranadaDirección de correo verificada de decsai.ugr.es
Sergio González VázquezPhD on Information & Communications Technology. Department of Computer Science and ArtificialDirección de correo verificada de decsai.ugr.es
Cristóbal J CarmonaAssociate Professor at University of JaenDirección de correo verificada de ujaen.es
Anabel Gómez-RíosUniversidad de GranadaDirección de correo verificada de decsai.ugr.es
Jerzy StefanowskiPoznan University of TechnologyDirección de correo verificada de cs.put.poznan.pl
María José Del Jesus DíazAssociate professor of Computer Science, University of JaénDirección de correo verificada de ujaen.es
Siham TabikDpt. Computer Science and Artificial Intelligence, University of GranadaDirección de correo verificada de ugr.es
Jose M. BenitezFull Professor, Universidad de GranadaDirección de correo verificada de decsai.ugr.es

Julián Luengo Martín

Associate Professor at University of Granada

Dirección de correo verificada de decsai.ugr.es

Artificial Intelligence Data Preprocessing in Data mining Missing Values Noise Data Data Complexity


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Alcalá-Fdez, A Fernández, J Luengo, J Derrac, S García, L Sánchez, ... Journal of Multiple-Valued Logic & Soft Computing 17, 2011	1811	2011
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power S García, A Fernández, J Luengo, F Herrera Information sciences 180 (10), 2044-2064, 2010	1356	2010
Data Preprocessing in Data Mining S García, J Luengo, F Herrera Springer, 2014	610	2014
A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability S García, A Fernández, J Luengo, F Herrera Soft Computing 13 (10), 959, 2009	591	2009
A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning S Garcia, J Luengo, JA Sáez, V Lopez, F Herrera IEEE Transactions on Knowledge and Data Engineering 25 (4), 734-750, 2012	418	2012
SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering JA Sáez, J Luengo, J Stefanowski, F Herrera Information Sciences 291, 184-203, 2015	219	2015
On the choice of the best imputation methods for missing values considering three groups of classification methods J Luengo, S García, F Herrera Knowledge and information systems 32 (1), 77-108, 2012	187	2012
Genetics-based machine learning for rule induction: state of the art, taxonomy, and comparative study A Fernández, S García, J Luengo, E Bernadó-Mansilla, F Herrera IEEE Transactions on Evolutionary Computation 14 (6), 913-941, 2010	165	2010
Big data preprocessing: methods and prospects S García, S Ramírez-Gallego, J Luengo, JM Benítez, F Herrera Big Data Analytics 1 (1), 9, 2016	161	2016
La educación como objeto de conocimiento. El concepto de educación J Luengo Teorías e instituciones contemporáneas de educación, 30-47, 2004	159	2004
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining S García, J Luengo, F Herrera Knowledge-Based Systems 98, 1-29, 2016	150	2016
Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling J Luengo, A Fernández, S García, F Herrera Soft Computing 15 (10), 1909-1936, 2011	138	2011
A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests J Luengo, S García, F Herrera Expert Systems with Applications 36 (4), 7798-7808, 2009	132	2009
Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition JA Sáez, M Galar, J Luengo, F Herrera Knowledge and information systems 38 (1), 179-206, 2014	105	2014
KEEL 3.0: an open source software for multi-stage analysis in data mining I Triguero, S González, JM Moyano, S García López, J Alcalá Fernández, ... Atlantis Press, 2017	97	2017
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 … J Luengo, S García, F Herrera Neural Networks 23 (3), 406-418, 2010	89	2010
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification JA SáEz, JN Luengo, F Herrera Pattern Recognition 46 (1), 355-364, 2013	86	2013
Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness JA Sáez, M Galar, JN Luengo, F Herrera Information Sciences 247, 1-20, 2013	83	2013
On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification I Triguero, JA Sáez, J Luengo, S García, F Herrera Neurocomputing 132, 30-41, 2014	69	2014
INFFC: an iterative class noise filter based on the fusion of classifiers with noise sensitivity control JA Sáez, M Galar, J Luengo, F Herrera Information Fusion 27, 19-32, 2016	59	2016

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