Alberto Fernández Hilario
Categoría
DoctorContacto
alberto@derMj@7iZQcsai.ugr.es
Organismo
UGR
Highly Cited Researcher en los años 2016 y 2017 Ver CV
| Total | Desde 2016: | |
|---|---|---|
| Citas | Total: 11872 | Desde 2016: 9476 |
| Índice H | Total: 40 | Desde 2016: 38 |
| Índice i10 | Total: 62 | Desde 2016: 55 |
Publicaciones (100)
| Título | Autores | Año |
|---|---|---|
| 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. | 2019 |
| A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems | S Elhag, A Fernández, A Altalhi, S Alshomrani, F Herrera. | 2019 |
| Allergy to vegetables belonging to the Solanaceae family | M Tomás-Pérez, I Hernández-Martín, API Fernández, MJ Pagola, .... | 2019 |
| SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary | A Fernandez, S Garcia, F Herrera, NV Chawla. | 2018 |
| Big data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce | S Ramírez-Gallego, A Fernández, S García, M Chen, F Herrera. | 2018 |
| Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach | S Vluymans, A Fernández, Y Saeys, C Cornelis, F Herrera. | 2018 |
| Imbalance: oversampling algorithms for imbalanced classification in R | I Cordón, S García, A Fernández, F Herrera. | 2018 |
| A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity | J Cózar, A Fernández, F Herrera, JA Gámez. | 2018 |
| A Pareto-based ensemble with feature and instance selection for learning from multi-class imbalanced datasets | A Fernández, CJ Carmona, M Jose del Jesus, F Herrera. | 2017 |
| 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, .... | 2017 |
| An insight into imbalanced Big Data classification: outcomes and challenges | A Fernández, S del Río, NV Chawla, F Herrera. | 2017 |
| Fuzzy rule based classification systems for big data with MapReduce: granularity analysis | A Fernández, S del Río, A Bawakid, F Herrera. | 2017 |
| NMC: nearest matrix classification–A new combination model for pruning One-vs-One ensembles by transforming the aggregation problem | M Galar, A Fernández, E Barrenechea, H Bustince, F Herrera. | 2017 |
| Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications? | A Fernández, S Alshomrani, A Altalhi, F Herrera. | 2017 |
| A view on fuzzy systems for big data: progress and opportunities | A Fernández, CJ Carmona, MJ del Jesus, F Herrera. | 2016 |
| Ordering-Based Pruning for Improving the Performance of Ensembles of Classifiers in the Framework of Imbalanced Datasets | M Galar, A Fernandez, E Barrenechea, H Bustince, H Francisco. | 2016 |
| Enhancing Evolutionary Fuzzy Systems for Multi-Class Problems: Distance-based Relative Competence Weighting with Truncated Confidences (DRCW-TC) | A Fernandez, M Elkano, M Galar, JA Sanz, S Alshomrani, H Bustince, .... | 2016 |
| Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification | A Fernández, F Herrera. | 2016 |
| Revisiting evolutionary fuzzy systems: Taxonomy, applications, new trends and challenges | A Fernandez, V Lopez, MJ del Jesus, F Herrera. | 2015 |
| 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. | 2015 |
| DRCW-OVO: distance-based relative competence weighting combination for one-vs-one strategy in multi-class problems | M Galar, A Fernández, E Barrenechea, F Herrera. | 2015 |
| A proposal for evolutionary fuzzy systems using feature weighting: dealing with overlapping in imbalanced datasets | S Alshomrani, A Bawakid, SO Shim, A Fernández, F Herrera. | 2015 |
| Propuesta de una asignatura de Diseño de Servidores para la especialidad de Tecnologías de Información | AJ Rivera Rivas, M Espinilla, A Fernández Hilario, J Santamaría López, .... | 2014 |
| 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, .... | 2014 |
| 2. Addressing the Data Intrinsic Characteristics of Imbalanced Problems using FRBCSs and Machine Learning Techniques | V López, A Fernández, MJ del Jesus, F Herrera. | 2014 |
| E-learning and educational data mining in cloud computing: an overview | A Fernández, D Peralta, JM Benítez, F Herrera. | 2014 |
| On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed | V López, A Fernández, F Herrera. | 2014 |
| Enhancing Multiclass Classification in FARC-HD Fuzzy Classifier: On the Synergy Between-Dimensional Overlap Functions and Decomposition Strategies | M Elkano, M Galar, JA Sanz, A Fernández, E Barrenechea, F Herrera, .... | 2014 |
| Empowering difficult classes with a similarity-based aggregation in multi-class classification problems | M Galar, A Fernández, E Barrenechea, F Herrera. | 2014 |
| Enhancing difficult classes in one-vs-one classifier fusion strategy using restricted equivalence functions | M Galar, E Barrenechea, A Fernández, F Herrera. | 2014 |
| Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches | A FernáNdez, V LóPez, M Galar, MAJ Del Jesus, F Herrera. | 2013 |
| A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets | V LóPez, A FernáNdez, MAJ Del Jesus, F Herrera. | 2013 |
| An overview on the structure and applications for business intelligence and data mining in cloud computing | A Fernández, S del Río, F Herrera, JM Benítez. | 2013 |
| 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. | 2013 |
| EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling | M Galar, A Fernández, E Barrenechea, F Herrera. | 2013 |
| IVTURS: A linguistic fuzzy rule-based classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection | JA Sanz, A Fernandez, H Bustince, F Herrera. | 2013 |
| Dynamic classifier selection for one-vs-one strategy: avoiding non-competent classifiers | M Galar, A Fernández, E Barrenechea, H Bustince, F Herrera. | 2013 |
| Addressing covariate shift for genetic fuzzy systems classifiers: a case of study with FARC-HD for imbalanced datasets | V López, A Fernández, F Herrera. | 2013 |
| Cost Sensitive and Preprocessing for Classification with Imbalanced Data-sets: Similar Behaviour and Potential Hybridizations. | V López, A Fernández, MJ Del Jesus, F Herrera. | 2012 |
| An overview of e-learning in cloud computing | A Fernandez, D Peralta, F Herrera, JM Benítez. | 2012 |
| 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. | 2012 |
| 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. | 2012 |
| Feature selection and granularity learning in genetic fuzzy rule-based classification systems for highly imbalanced data-sets | P Villar, A Fernandez, RA Carrasco, F Herrera. | 2012 |
| IIVFDT: Ignorance functions based interval-valued fuzzy decision tree with genetic tuning | J Sanz, H Bustince, A Fernández, F Herrera. | 2012 |
| Linguistic fuzzy rules in data mining: follow-up Mamdani fuzzy modeling principle | A Fernández, F Herrera. | 2012 |
| On the usefulness of fuzzy rule based systems based on hierarchical linguistic fuzzy partitions | A Fernández, V López, MJ Del Jesus, F Herrera. | 2011 |
| Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling | J Luengo, A Fernández, S García, F Herrera. | 2011 |
| Using KEEL software as a educational tool: A case of study teaching data mining | J Derrac, J Luengo, J Alcala-Fdez, A Fernandez, S Garcia. | 2011 |
| KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework | J Alcala-Fdez, A Fernández, J Luengo, J Derrac, S García, L Sánchez, .... | 2011 |
| 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. | 2011 |
| A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: Degree of ignorance and lateral position | J Sanz, A Fernández, H Bustince, F Herrera. | 2011 |
| Addressing the classification with imbalanced data: open problems and new challenges on class distribution | A Fernández, S García, F Herrera. | 2011 |
| Aggregation Schemes for Binarization Techniques Methods' Description | M Galar, A Fernández, E Barrenechea, H Bustince, F Herrera. | 2011 |
| A case study on medical diagnosis of cardiovascular diseases using a genetic algorithm for tuning fuzzy rule-based classification systems with interval-valued fuzzy sets | J Sanz, M Pagola, H Bustince, A Brugos, A Fernández, F Herrera. | 2011 |
| Construction of interval-valued fuzzy preference relations using ignorance functions: Interval-valued non dominance criterion | E Barrenechea, A Fernández, F Herrera, H Bustince. | 2011 |
| Studying the behavior of a multiobjective genetic algorithm to design fuzzy rule-based classification systems for imbalanced data-sets | P Villar, A Fernández, F Herrera. | 2011 |
| On the cooperation of interval-valued fuzzy sets and genetic tuning to improve the performance of fuzzy decision trees | JA Sanz, H Bustince, A Fernández, F Herrera. | 2011 |
| On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets | A Fernández, MJ del Jesus, F Herrera. | 2010 |
| Multi-class imbalanced data-sets with linguistic fuzzy rule based classification systems based on pairwise learning | A Fernández, MJ Del Jesus, F Herrera. | 2010 |
| Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets | MD Pérez-Godoy, A Fernández, AJ Rivera, MJ del Jesus. | 2010 |
| Analysing the hierarchical fuzzy rule based classification systems with genetic rule selection | A Fernández, MJ del Jesús, F Herrera. | 2010 |
| 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. | 2010 |
| 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. | 2010 |
| Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning | JA Sanz, A Fernández, H Bustince, F Herrera. | 2010 |
| Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations | A Fernández, M Calderón, E Barrenechea, H Bustince, F Herrera. | 2010 |
| A genetic algorithm for tuning fuzzy rule-based classification systems with interval-valued fuzzy sets | J Sanz, A Fernández, H Bustince, F Herrera. | 2010 |
| A genetic algorithm for feature selection and granularity learning in fuzzy rule-based classification systems for highly imbalanced data-sets | P Villar, A Fernández, F Herrera. | 2010 |
| A first approach for cost-sensitive classification with linguistic genetic fuzzy systems in imbalanced data-sets | V López, A Fernández, F Herrera. | 2010 |
| USING SIMILARITY MEASURES IN FUZZY RULE-BASED CLASSIFICATION SYSTEMS WITH INTERVAL-VALUED FUZZY SETS | J Sanz, D Jurío, A Fernández, F Herrera, H Bustince. | 2010 |
| Sistemas de clasificación basados en reglas difusas lingüísticas aplicadas a problemas con clases no balanceadas | A Fernández Hilario. | 2010 |
| Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets | A Fernández, MJ del Jesus, F Herrera. | 2009 |
| On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets | A Fernández, MJ del Jesus, F Herrera. | 2009 |
| Improving the performance of fuzzy rule based classification systems for highly imbalanced data-sets using an evolutionary adaptive inference system | A Fernández, MJ del Jesus, F Herrera. | 2009 |
| A Preliminar Analysis of CO2RBFN in Imbalanced Problems | MD Pérez-Godoy, AJ Rivera, A Fernández, MJ del Jesús, F Herrera. | 2009 |
| A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability | S García, A Fernández, J Luengo, F Herrera. | 2009 |
| Addressing data-complexity for imbalanced data-sets: A preliminary study on the use of preprocessing for c4. 5 | J Luengo, A Fernández, S García, F Herrera. | 2009 |
| Implementation and integration of algorithms into the KEEL data-mining software tool | A Fernández, J Luengo, J Derrac, J Alcalá-Fdez, F Herrera. | 2009 |
| Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems | S García, A Fernández, F Herrera. | 2009 |
| Enhancing fuzzy rule based systems in multi-classification using pairwise coupling with preference relations | A Fernández, M Calderón, E Barrenechea, H Bustince, F Herrera. | 2009 |
| A genetic learning of the fuzzy rule-based classification system granularity for highly imbalanced data-sets | P Villar, A Fernández, F Herrera. | 2009 |
| A First Study on the Use of Interval-Valued Fuzzy Sets with Genetic Tuning for Classification with Imbalanced Data-Sets | J Sanz, A Fernández, H Bustince, F Herrera. | 2009 |
| Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets | A Fernández, FJ Berlanga, MJ del Jesus, F Herrera. | 2009 |
| Un algoritmo genético para selección de características en sistemas de clasificación basados en reglas difusas para conjuntos de datos altamente no balanceados | P Villar, A Fernández, A Sánchez, F Herrera. | 2009 |
| 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. | 2008 |
| KEEL: A data mining software tool integrating genetic fuzzy systems | J Alcalá-Fdez, S García, FJ Berlanga, A Fernández, L Sánchez, .... | 2008 |
| A short study on the use of genetic 2-tuples tuning for fuzzy rule based classification systems in imbalanced data-sets | A Fernández, MJ del Jesus, F Herrera. | 2008 |
| An analysis of the rule weights and fuzzy reasoning methods for linguistic rule based classification systems applied to problems with highly imbalanced data sets | A Fernández, S García, F Herrera, MJ del Jesús. | 2007 |
| A study on the use of the fuzzy reasoning method based on the winning rule vs. voting procedure for classification with imbalanced data sets | A Fernández, S García, MJ Del Jesús, F Herrera. | 2007 |
| Fuzzy Machine Learning-An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced … | A Fernandez, S Garcia, F Herrera, MJ Jesus. | 2007 |
| Statistical comparisons by means of non-parametric tests: a case study on genetic based machine learning | S García, AD Benítez, F Herrera, A Fernández. | 2007 |
| A proposal of evolutionary prototype selection for class imbalance problems | S García, JR Cano, A Fernández, F Herrera. | 2006 |
| A First Study on the Use of Fuzzy Rule Based Classification Systems for Problems with Imbalanced Data Sets | MJ del Jesus, A Fernández, S Garcıa, F Herrera. | 2006 |
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