Julián Luengo Martín - Citas de 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 FernándezAssistant 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 skimlinks.com
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
Isaac Triguero VelázquezAssistant Professor in Data Science, School of Computer Science, University of NottinghamDirección de correo verificada de nottingham.ac.uk
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
Cristóbal J CarmonaAssociate Professor at University of JaenDirección de correo verificada de ujaen.es
Sergio González VázquezPhD Student, Department of Computer Science and Artificial Intelligence, University of GranadaDirección de correo verificada de decsai.ugr.es
Jerzy StefanowskiPoznan University of TechnologyDirección de correo verificada de cs.put.poznan.pl
Jesús Maillo HidalgoPHD Student, Department of Computer Science and Artificial Intelligence, University of GranadaDirección de correo verificada de decsai.ugr.es
María José Del Jesus DíazAssociate professor of Computer Science, University of JaénDirección de correo verificada de ujaen.es
Diego García-GilPh.D. Student in Big Data & Data Science, University of GranadaDirección de correo verificada de decsai.ugr.es
Anabel Gómez-RíosUniversidad de GranadaDirección de correo verificada de decsai.ugr.es
Sergio Ramírez-GallegoPhD. Student Computer Science, University of GranadaDirección de correo verificada de decsai.ugr.es
Jose M. BenitezFull Professor, Universidad de GranadaDirección de correo verificada de decsai.ugr.es
Siham TabikDpt. Computer Science and Artificial Intelligence. University of GranadaDirección de correo verificada de uma.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	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	1601	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	1199	2010
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	533	2009
Data Preprocessing in Data Mining S García, J Luengo, F Herrera Springer, 2014	450	2014
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	361	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	181	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	166	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	149	2010
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	124	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	121	2009
Big data preprocessing: methods and prospects S García, S Ramírez-Gallego, J Luengo, JM Benítez, F Herrera Big Data Analytics 1 (1), 9, 2016	110	2016
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	108	2016
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	93	2014
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	80	2010
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	73	2013
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	72	2013
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	66	2017
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	57	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	50	2016
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 J Luengo, F Herrera Fuzzy Sets and Systems 161 (1), 3-19, 2010	46	2010

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