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Salvador García
Salvador García
Full Professor of Computer Science and Artificial Intelligence. University of Granada.
在 decsai.ugr.es 的电子邮件经过验证
标题
引用次数
引用次数
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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
AB Arrieta, N Díaz-Rodríguez, J Del Ser, A Bennetot, S Tabik, A Barbado, ...
Information fusion 58, 82-115, 2020
90842020
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
J Derrac, S García, D Molina, F Herrera
Swarm and Evolutionary Computation 1 (1), 3-18, 2011
54052011
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, 255-287, 2015
30292015
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
23202010
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
A Fernández, S Garcia, F Herrera, NV Chawla
Journal of artificial intelligence research 61, 863-905, 2018
20582018
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
V López, A Fernández, S García, V Palade, F Herrera
Information sciences 250, 113-141, 2013
19382013
Data Preprocessing in Data Mining
S García, J Luengo, F Herrera
Springer, 2015
18742015
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
S García, D Molina, M Lozano, F Herrera
Journal of Heuristics 15, 617-644, 2009
18132009
An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons
S García, F Herrera
Journal of Machine Learning Research 9, 2677-2694, 2008
17862008
KEEL: a software tool to assess evolutionary algorithms for data mining problems
J Alcalá-Fdez, L Sanchez, S Garcia, MJ del Jesus, S Ventura, JM Garrell, ...
Soft Computing 13, 307-318, 2009
17412009
Learning from Imbalanced Data Sets
A Fernández, S García, M Galar, RC Prati, B Krawczyk, F Herrera
Springer, 2018
14712018
Prototype selection for nearest neighbor classification: Taxonomy and empirical study
S Garcia, J Derrac, J Cano, F Herrera
IEEE transactions on pattern analysis and machine intelligence 34 (3), 417-435, 2012
8302012
Big data preprocessing: methods and prospects
S García, S Ramírez-Gallego, J Luengo, JM Benítez, F Herrera
Big data analytics 1, 1-22, 2016
8292016
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
8102009
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
I Triguero, S García, F Herrera
Knowledge and Information Systems 42 (2), 245-284, 2015
7172015
A survey of discretization techniques: taxonomy and empirical analysis in supervised learning
S García, J Luengo, J Saez, V Lopez, F Herrera
IEEE Transactions on Knowledge and Data Engineering 25 (4), 734-750, 2013
7042013
A survey on data preprocessing for data stream mining: Current status and future directions
S Ramírez-Gallego, B Krawczyk, S García, M Woźniak, F Herrera
Neurocomputing 239, 39-57, 2017
5682017
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
S García, F Herrera
Evolutionary computation 17 (3), 275-306, 2009
5182009
Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review
J Carrasco, S García, MM Rueda, S Das, F Herrera
Swarm and Evolutionary Computation 54, 100665, 2020
4672020
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities
S González, S García, J Del Ser, L Rokach, F Herrera
Information Fusion 64, 205-237, 2020
4562020
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