What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
André C. P. L. F. de Carvalho mostly deals with Artificial intelligence, Machine learning, Data mining, Pattern recognition and One-class classification.
He regularly ties together related areas like Algorithm in his Artificial intelligence studies.
His work on Decision tree is typically connected to Computer programming as part of general Machine learning study, connecting several disciplines of science.
His work in Data mining addresses subjects such as Cluster analysis, which are connected to disciplines such as Quality.
His study in Pattern recognition is interdisciplinary in nature, drawing from both RNA, microRNA, Feature and Identification.
His Data stream mining research includes themes of Network intrusion detection, Wireless sensor network, Knowledge extraction and Data science.
His most cited work include:
- Knowledge Discovery from Data Streams. (546 citations)
- Data stream clustering: A survey (323 citations)
- A review on the combination of binary classifiers in multiclass problems (177 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Data mining, Pattern recognition and Artificial neural network.
Artificial intelligence is often connected to Algorithm in his work.
As part of his studies on Machine learning, André C. P. L. F. de Carvalho frequently links adjacent subjects like Classifier.
His Data mining research is multidisciplinary, relying on both Unsupervised learning and Clustering high-dimensional data, Cluster analysis.
His Pattern recognition research integrates issues from Noise and Identification.
When carried out as part of a general Data stream mining research project, his work on Concept drift is frequently linked to work in Novelty detection, therefore connecting diverse disciplines of study.
He most often published in these fields:
- Artificial intelligence (60.14%)
- Machine learning (41.77%)
- Data mining (22.20%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (60.14%)
- Machine learning (41.77%)
- Cluster analysis (11.93%)
In recent papers he was focusing on the following fields of study:
André C. P. L. F. de Carvalho focuses on Artificial intelligence, Machine learning, Cluster analysis, Decision tree and Pattern recognition.
André C. P. L. F. de Carvalho has included themes like Data stream mining and Complex network in his Artificial intelligence study.
In general Data stream mining, his work in Concept drift is often linked to Novelty detection linking many areas of study.
His Machine learning research focuses on subjects like Interpretation, which are linked to Decision-making and Feature vector.
As a member of one scientific family, André C. P. L. F. de Carvalho mostly works in the field of Cluster analysis, focusing on Data mining and, on occasion, Data stream.
His Decision tree research is multidisciplinary, incorporating perspectives in Tree, Algorithm and Hash function.
Between 2018 and 2021, his most popular works were:
- Ensemble of Classifiers Based on Multiobjective Genetic Sampling for Imbalanced Data (16 citations)
- A new data characterization for selecting clustering algorithms using meta-learning (15 citations)
- Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Meta learning and Cluster analysis.
His Artificial intelligence study frequently intersects with other fields, such as Identification.
His biological study deals with issues like Complex network, which deal with fields such as Node.
His Pattern recognition research incorporates elements of Filter, Selection and Noise.
His Binary classification study integrates concerns from other disciplines, such as Evolutionary algorithm, Multiclass classification and Imbalanced data.
His Process study combines topics in areas such as Data mining, Linear separability, Search engine, Recommender system and Function.
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