Julián Luengo Martín
Julián Luengo Martín
Associate Professor at University of Granada
Dirección de correo verificada de decsai.ugr.es
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Citado por
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
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
Data Preprocessing in Data Mining
S García, J Luengo, F Herrera
Springer, 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
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
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
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
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
Big data preprocessing: methods and prospects
S García, S Ramírez-Gallego, J Luengo, JM Benítez, F Herrera
Big Data Analytics 1 (1), 9, 2016
La educación como objeto de conocimiento. El concepto de educación
J Luengo
Teorías e instituciones contemporáneas de educación, 30-47, 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
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
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
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
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
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
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
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
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
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
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