H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 55 Citations 24,236 150 World Ranking 2219 National Ranking 23

Overview

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.

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.

Top Publications

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)

2538 Citations

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)

1838 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

Salvador García;Daniel Molina;Manuel Lozano;Francisco Herrera.
Journal of Heuristics (2009)

1375 Citations

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)

1374 Citations

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)

1288 Citations

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)

1209 Citations

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)

984 Citations

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)

636 Citations

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)

632 Citations

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)

590 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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