What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Computer programming, Machine learning, Data mining and Genetic algorithm.
The Artificial intelligence study combines topics in areas such as Perspective and Heuristics.
The concepts of his Computer programming study are interwoven with issues in Preprocessor, Knowledge representation and reasoning, Association rule learning, Data stream mining and Data set.
His research integrates issues of Algorithm, Ant colony optimization algorithms and Knowledge extraction in his study of Machine learning.
His Data mining research includes themes of Statistical classification, Genetic programming, Set and Ant colony.
His Genetic algorithm study combines topics from a wide range of disciplines, such as Variable, Phenotype, Genotype, Facility location problem and Chromosome.
His most cited work include:
- Data mining with an ant colony optimization algorithm (815 citations)
- Data Mining and Knowledge Discovery with Evolutionary Algorithms (594 citations)
- A survey of hierarchical classification across different application domains (553 citations)
What are the main themes of his work throughout his whole career to date?
Alex A. Freitas spends much of his time researching Artificial intelligence, Machine learning, Data mining, Computer programming and Genetic programming.
His Artificial intelligence research integrates issues from Genetic algorithm and Pattern recognition.
His Machine learning study incorporates themes from Algorithm and Ant colony optimization algorithms.
Class is closely connected to Set in his research, which is encompassed under the umbrella topic of Data mining.
His study looks at the intersection of Computer programming and topics like Artificial immune system with Fuzzy rule.
As part of the same scientific family, Alex A. Freitas usually focuses on Evolutionary algorithm, concentrating on Evolutionary computation and intersecting with Memetic algorithm.
He most often published in these fields:
- Artificial intelligence (59.78%)
- Machine learning (46.83%)
- Data mining (35.54%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (59.78%)
- Machine learning (46.83%)
- Data mining (35.54%)
In recent papers he was focusing on the following fields of study:
Alex A. Freitas mainly investigates Artificial intelligence, Machine learning, Data mining, Pattern recognition and Classifier.
Artificial intelligence is a component of his Statistical classification, Feature selection, Class, Decision tree and Naive Bayes classifier studies.
Evolutionary algorithm, Bayesian network, Multi-label classification, Genetic programming and Interpretability are the primary areas of interest in his Machine learning study.
Alex A. Freitas usually deals with Evolutionary algorithm and limits it to topics linked to Rule induction and Set.
His Data mining study frequently draws connections to adjacent fields such as Longitudinal study.
His Pattern recognition research is multidisciplinary, incorporating elements of Genetic algorithm and Probabilistic logic.
Between 2014 and 2021, his most popular works were:
- A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood (104 citations)
- The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (82 citations)
- An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives (60 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Feature selection, Pattern recognition and Classifier.
His work on Model organism expands to the thematically related Artificial intelligence.
He focuses mostly in the field of Machine learning, narrowing it down to topics relating to Gene ontology and, in certain cases, Directed acyclic graph.
He combines subjects such as Neighbourhood, Molecular descriptor, Data mining and Naive Bayes classifier with his study of Feature selection.
His Decision tree study in the realm of Data mining connects with subjects such as sort.
Within one scientific family, Alex A. Freitas focuses on topics pertaining to Genetic algorithm under Pattern recognition, and may sometimes address concerns connected to Classifier chains.
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