What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Machine learning, Fuzzy logic, Data mining and Fuzzy set.
His Artificial intelligence study typically links adjacent topics like Linguistics.
The concepts of his Machine learning study are interwoven with issues in Set and Genetic fuzzy systems.
As a part of the same scientific study, he usually deals with the Set, concentrating on Nonparametric statistics and frequently concerns with Statistical hypothesis testing.
His Fuzzy logic study integrates concerns from other disciplines, such as Multiplicative function, Group decision-making and Preference, Preference theory.
In Data mining, he works on issues like Data set, which are connected to Algorithm.
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)
- A 2-tuple fuzzy linguistic representation model for computing with words (1781 citations)
- KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework (1422 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Machine learning, Data mining, Fuzzy logic and Fuzzy rule.
His research links Pattern recognition with Artificial intelligence.
Francisco Herrera has researched Machine learning in several fields, including Classifier and Set.
The various areas that Francisco Herrera examines in his Data mining study include Class, Preprocessor, Feature selection and k-nearest neighbors algorithm.
His work deals with themes such as Mathematical optimization, Preference and Group decision-making, which intersect with Fuzzy logic.
His work on Rule-based machine translation as part of his general Linguistics study is frequently connected to Term, thereby bridging the divide between different branches of science.
He most often published in these fields:
- Artificial intelligence (66.54%)
- Machine learning (42.33%)
- Data mining (27.91%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (66.54%)
- Machine learning (42.33%)
- Big data (9.15%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Machine learning, Big data, Data mining and Group decision-making.
He has included themes like Natural language processing, Set and Pattern recognition in his Artificial intelligence study.
Many of his studies on Machine learning involve topics that are commonly interrelated, such as Optimization problem.
As part of one scientific family, Francisco Herrera deals mainly with the area of Big data, narrowing it down to issues related to the Data science, and often Taxonomy.
His Group decision-making research is multidisciplinary, incorporating perspectives in Consistency, Social network, Cluster analysis, Fuzzy logic and Preference.
His work in Preference addresses subjects such as Set, which are connected to disciplines such as Linguistics.
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)
- Bio-inspired computation: Where we stand and what's next (172 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Francisco Herrera mainly investigates Artificial intelligence, Group decision-making, Machine learning, Term and Preference.
His Artificial intelligence study frequently draws connections to adjacent fields such as Pattern recognition.
His research integrates issues of Consistency, Social network, Cluster analysis, Fuzzy logic and Operations research in his study of Group decision-making.
His Fuzzy logic research includes elements of Multiplicative function and Consistency.
He is interested in Ensemble selection, which is a branch of Machine learning.
His work investigates the relationship between Convolutional neural network and topics such as Robustness that intersect with problems in Data mining.
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