Alberto Fernández Hilario
Alberto Fernández Hilario
Full Professor of Computer Science and Artificial Intelligence, University of Granada
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A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches
M Galar, A Fernández, E Barrenechea, H Bustince, F Herrera
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE …, 2012
KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework
J Alcalá-Fdez, A Fernández, J Luengo, J Derrac, ...
Journal of Multiple-Valued Logic and Soft Computing 17 (2-3), 255-287, 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
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
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
A Fernandez, S Garcia, F Herrera, NV Chawla
Journal of Artificial Intelligence Research 61, 863-905, 2018
Learning from imbalanced data sets
A Fernández, S García, M Galar, RC Prati, B Krawczyk, F Herrera
Springer 10 (2018), 2018
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
M Galar, A Fernández, E Barrenechea, H Bustince, F Herrera
Pattern Recognition 44 (8), 1761-1776, 2011
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
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
M Galar, A Fernández, E Barrenechea, F Herrera
Pattern recognition 46 (12), 3460-3471, 2013
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
A Fernández, V López, M Galar, M José del Jesus, F Herrera
Knowledge-Based Systems, 2013
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics
V López, A Fernández, JG Moreno-Torres, F Herrera
Expert Systems with Applications 39 (7), 6585-6608, 2012
A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets
A Fernández, S García, MJ del Jesus, F Herrera
Fuzzy Sets and Systems 159 (18), 2378-2398, 2008
Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks
A Fernández, S del Río, V López, A Bawakid, MJ del Jesus, JM Benítez, ...
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4 (5 …, 2014
KEEL 3.0: an open source software for multi-stage analysis in data mining
I Triguero, S González, JM Moyano, S García, J Alcalá-Fdez, J Luengo, ...
International Journal of Computational Intelligence Systems 10 (1), 1238-1249, 2017
On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems
S Elhag, A Fernández, A Bawakid, S Alshomrani, F Herrera
Expert Systems with Applications 42 (1), 193-202, 2015
An insight into imbalanced Big Data classification: outcomes and challenges
A Fernández, S del Río, NV Chawla, F Herrera
Complex & Intelligent Systems 3, 105-120, 2017
Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to?
A Fernandez, F Herrera, O Cordon, MJ del Jesus, F Marcelloni
IEEE Computational intelligence magazine 14 (1), 69-81, 2019
Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets
A Fernández, MJ del Jesus, F Herrera
International Journal of Approximate Reasoning 50 (3), 561-577, 2009
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-A Fusion of Foundations, Methodologies and Applications 15 …, 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
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