Alberto Fernández spends much of his time researching Machine learning, Artificial intelligence, Data mining, Imbalanced data and Class. The study of Machine learning is intertwined with the study of Classifier in a number of ways. The Classifier study combines topics in areas such as Algorithm design, Undersampling and Class imbalance.
His work in the fields of Artificial intelligence, such as Rule of inference, intersects with other areas such as Multiple comparisons problem, Nonparametric statistics and Statistical hypothesis testing. Alberto Fernández has researched Data mining in several fields, including Contrast, Fuzzy rule, Soft computing, Fuzzy logic and Fuzzy classification. Alberto Fernández interconnects Preprocessing algorithm and Big data in the investigation of issues within Imbalanced data.
His primary areas of study are Artificial intelligence, Machine learning, Data mining, Fuzzy rule and Fuzzy logic. Fuzzy classification, Fuzzy control system, Classifier, Fuzzy set and Evolutionary algorithm are subfields of Artificial intelligence in which his conducts study. His Classifier study combines topics in areas such as Boosting and Class imbalance.
His work investigates the relationship between Machine learning and topics such as Preprocessor that intersect with problems in Undersampling. His study looks at the relationship between Data mining and topics such as Computational intelligence, which overlap with Intrusion detection system. His work carried out in the field of Fuzzy rule brings together such families of science as Knowledge-based systems, Neuro-fuzzy, Knowledge base and Big data.
His primary scientific interests are in Artificial intelligence, Machine learning, Big data, Data mining and Fuzzy logic. Artificial intelligence and Pattern recognition are frequently intertwined in his study. His research in Machine learning focuses on subjects like Preprocessor, which are connected to Undersampling.
His Big data research is multidisciplinary, incorporating elements of Context, Fuzzy rule and Data science. His Fuzzy logic research focuses on Robustness and how it relates to Decision rule, Linear programming, Cluster analysis and Computation. His biological study spans a wide range of topics, including Decision tree, Statistical classification, Training set and Class imbalance.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Big data, Data mining and Evolutionary algorithm. His study involves Computational intelligence, Imbalanced data, Classifier and Fuzzy logic, a branch of Artificial intelligence. His Computational intelligence research incorporates elements of State, Fuzzy control system and Intrusion prevention system.
Binary case and Cyber-attack are fields of study that intersect with his Machine learning research. His Big data research is multidisciplinary, relying on both Fuzzy rule and Data science. His Data mining research is multidisciplinary, incorporating perspectives in Context and Feature selection.
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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
M. Galar;A. Fernandez;E. Barrenechea;H. Bustince.
systems man and cybernetics (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.
soft computing (2011)
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
Salvador García;Alberto Fernández;Julián Luengo;Francisco Herrera.
Information Sciences (2010)
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
Victoria López;Alberto Fernández;Salvador García;Vasile Palade.
Information Sciences (2013)
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
Mikel Galar;Alberto Fernández;Edurne Barrenechea;Humberto Bustince.
Pattern Recognition (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 (2009)
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
Alberto Fernández;Salvador García;Francisco Herrera;Nitesh V. Chawla.
Journal of Artificial Intelligence Research (2018)
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
Mikel Galar;Alberto Fernández;Edurne Barrenechea;Francisco Herrera.
Pattern Recognition (2013)
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
Alberto FernáNdez;Victoria LóPez;Mikel Galar;MaríA José Del Jesus.
Knowledge Based Systems (2013)
A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets
Alberto Fernández;Salvador García;María José del Jesus;Francisco Herrera.
Fuzzy Sets and Systems (2008)
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