Julián Luengo Martín

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Francisco HerreraProfessor Computer Science and AI, Granada University, SpainVerified email at decsai.ugr.es
Salvador GarcíaFull Professor of Computer Science and Artificial Intelligence. University of Granada.Verified email at decsai.ugr.es
Alberto FernándezAssociate Professor in Computer Science and Artificial Intelligence, University of GranadaVerified email at decsai.ugr.es
José A. SáezDept. of Statistics and Operations Research, University of GranadaVerified email at ugr.es
Joaquin Derrac RusData ScientistVerified email at gresearch.co.uk
Diego García-GilAssistant Professor, University of GranadaVerified email at ugr.es
Sergio Ramírez-GallegoPhD. ML and Computer ScienceVerified email at thalesaleniaspace.com
Luciano SanchezProfessor of Computer Science, Oviedo UniversityVerified email at uniovi.es
Isaac Triguero VelázquezDistinguished Senior Research Fellow at University of Granada, Associate Professor in Data ScienceVerified email at nottingham.ac.uk
Anabel Gómez-RíosUniversidad de GranadaVerified email at decsai.ugr.es
Jesus Alcala-FdezAssociate Professor of Computer Science and Artificial Intelligence, University of GranadaVerified email at decsai.ugr.es
Siham TabikAssociate Professor, Dpt. Computer Science and Artificial Intelligence, University of GranadaVerified email at ugr.es
Mikel GalarAssociate Professor of Computer Science and Artificial Intelligence, Universidad Pública de NavarraVerified email at unavarra.es
Jesús Maillo HidalgoPh.D. Department of Computer Science and Artificial Intelligence, University of GranadaVerified email at decsai.ugr.es
Victoria Lopez MoralesResearch Associate, Imperial College LondonVerified email at imperial.ac.uk
José Ramón Cano De AmoUniversity of JaenVerified email at ujaen.es
Sergio González VázquezPhD on Information & Communications Technology. Department of Computer Science and ArtificialVerified email at decsai.ugr.es
Jerzy StefanowskiPoznan University of TechnologyVerified email at cs.put.poznan.pl
María José Del Jesus DíazAssociate professor of Computer Science, University of JaénVerified email at ujaen.es
ASM ShihavuddinProfessor, EEE, Green University of Bangladesh (GUB)Verified email at eee.green.edu.bd

Julián Luengo Martín

Associate Professor at University of Granada

Verified email at decsai.ugr.es

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


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Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework J Derrac, S Garcia, L Sanchez, F Herrera J. Mult. Valued Logic Soft Comput 17, 2015	2581	2015
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	1906	2010
Data Preprocessing in Data Mining S García, J Luengo, F Herrera Springer, 2014	1226	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, 959-977, 2009	716	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	582	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	470	2015
Big data preprocessing: methods and prospects S García, S Ramírez-Gallego, J Luengo, JM Benítez, F Herrera Big Data Analytics 1 (1), 1-22, 2016	466	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	275	2016
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, 77-108, 2012	273	2012
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images S Tabik, A Gómez-Ríos, JL Martín-Rodríguez, I Sevillano-García, ... IEEE journal of biomedical and health informatics 24 (12), 3595-3605, 2020	255	2020
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	219	2017
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, 1909-1936, 2011	187	2011
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	185	2010
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	172	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, 179-206, 2014	143	2014
Enabling smart data: noise filtering in big data classification D García-Gil, J Luengo, S García, F Herrera Information Sciences 479, 135-152, 2019	140	2019
Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness JA Sáez, M Galar, J Luengo, F Herrera Information Sciences 247, 1-20, 2013	115	2013
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	115	2010
Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data I Triguero, D García‐Gil, J Maillo, J Luengo, S García, F Herrera Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9 (2 …, 2019	114	2019
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification JA Sáez, J Luengo, F Herrera Pattern Recognition 46 (1), 355-364, 2013	107	2013

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