What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
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.
His most cited work include:
- A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms (2352 citations)
- KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework (1422 citations)
- 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 (1198 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (71.75%)
- Machine learning (65.54%)
- Data mining (44.07%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (71.75%)
- Machine learning (65.54%)
- Big data (16.95%)
In recent papers he was focusing on the following fields of 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.
Between 2017 and 2021, his most popular works were:
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (496 citations)
- SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary (253 citations)
- A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines (104 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
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.
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