His primary scientific interests are in Artificial intelligence, Machine learning, Data mining, Set and Taxonomy. His study ties his expertise on Field together with the subject of Artificial intelligence. When carried out as part of a general Machine learning research project, his work on Evolutionary algorithm and Computational intelligence is frequently linked to work in Nonparametric statistics and Statistical hypothesis testing, therefore connecting diverse disciplines of study.
The Evolutionary algorithm study combines topics in areas such as Evolutionary computation and Optimization problem. His work deals with themes such as Preprocessor, Reduction, Missing data and Data set, which intersect with Data mining. His Data set research is multidisciplinary, incorporating elements of Algorithm, Categorization and Data pre-processing.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Data mining, Evolutionary algorithm and k-nearest neighbors algorithm. Salvador García integrates several fields in his works, including Artificial intelligence and Nonparametric statistics. His work on Computational intelligence as part of general Machine learning study is frequently linked to Statistical hypothesis testing, bridging the gap between disciplines.
His research on Data mining frequently links to adjacent areas such as Data set. His Evolutionary algorithm study combines topics from a wide range of disciplines, such as Genetic algorithm, Fitness function, Instance selection, Rule induction and Feature selection. He has included themes like Reduction, Differential evolution, Supervised learning and Fuzzy logic in his k-nearest neighbors algorithm study.
Artificial intelligence, Machine learning, Big data, Data mining and Data pre-processing are his primary areas of study. His research brings together the fields of Field and Artificial intelligence. His work carried out in the field of Machine learning brings together such families of science as Class and Set.
His Big data study incorporates themes from Scalability, Preprocessor and k-nearest neighbors algorithm. Salvador García studies Data mining, focusing on Knowledge extraction in particular. His Data pre-processing research incorporates themes from Training set and Feature selection.
His primary scientific interests are in Artificial intelligence, Machine learning, Big data, Algorithm and Data mining. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Variables. He interconnects Software and Snapshot in the investigation of issues within Machine learning.
His Big data research is multidisciplinary, relying on both Scalability, Preprocessing algorithm and Imbalanced data. His study in the fields of IEEE Congress on Evolutionary Computation under the domain of Algorithm overlaps with other disciplines such as Task, Context and Statistical hypothesis testing. His Data mining research includes elements of Minimum description length and Multivariate statistics.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
Joaquín Derrac;Salvador García;Daniel Molina;Francisco Herrera.
Swarm and evolutionary computation (2011)
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)
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
Salvador García;Daniel Molina;Manuel Lozano;Francisco Herrera.
Journal of Heuristics (2009)
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 Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons
Salvador García;Francisco Herrera.
Journal of Machine Learning Research (2008)
KEEL: a software tool to assess evolutionary algorithms for data mining problems
J. Alcalá-Fdez;L. Sánchez;S. García;M. J. del Jesus.
soft computing (2008)
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)
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)
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
Salvador Garcia;Joaquin Derrac;Jose Ramon Cano;Francisco Herrera.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta;Natalia Díaz-Rodríguez;Javier Del Ser;Javier Del Ser;Adrien Bennetot;Adrien Bennetot.
Information Fusion (2020)
Profile was last updated on December 6th, 2021.
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