What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His main research concerns Artificial intelligence, Machine learning, Classifier, Data mining and Pattern recognition.
In his works, Bartosz Krawczyk undertakes multidisciplinary study on Artificial intelligence and Coral species.
In the field of Classifier, his study on Quadratic classifier and Linear classifier overlaps with subjects such as Linear subspace.
His Data mining research focuses on Multiclass classification and how it connects with Data set and Class.
In general Pattern recognition study, his work on Random subspace method and Feature vector often relates to the realm of Breast cancer, thereby connecting several areas of interest.
His work on Concept drift as part of his general Data stream mining study is frequently connected to Complex data type and Information system, thereby bridging the divide between different branches of science.
His most cited work include:
- Learning from imbalanced data: open challenges and future directions (642 citations)
- Ensemble learning for data stream analysis (411 citations)
- Cost-sensitive decision tree ensembles for effective imbalanced classification (178 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, Classifier, Pattern recognition and Data mining.
His work on Data stream mining, Artificial neural network and Imbalanced data as part of general Machine learning research is often related to Linear subspace, thus linking different fields of science.
His study looks at the relationship between Data stream mining and topics such as Big data, which overlap with Data science.
Bartosz Krawczyk combines subjects such as Evolutionary algorithm and Decision tree with his study of Classifier.
His work on Feature extraction, Feature selection and Segmentation as part of general Pattern recognition research is frequently linked to Breast cancer, bridging the gap between disciplines.
His biological study spans a wide range of topics, including Computational complexity theory, Online machine learning, Benchmark and k-nearest neighbors algorithm.
He most often published in these fields:
- Artificial intelligence (84.95%)
- Machine learning (72.04%)
- Classifier (47.31%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (84.95%)
- Machine learning (72.04%)
- Data stream mining (18.82%)
In recent papers he was focusing on the following fields of study:
Bartosz Krawczyk mainly investigates Artificial intelligence, Machine learning, Data stream mining, Classifier and Concept drift.
In general Artificial intelligence, his work in Cluster analysis, Data set and Convolutional neural network is often linked to Process and Field linking many areas of study.
His Machine learning study frequently draws connections between related disciplines such as Big data.
His work carried out in the field of Data stream mining brings together such families of science as Active learning and Overfitting.
His studies deal with areas such as Computational complexity theory, Unsupervised learning and Adaptive learning as well as Classifier.
His Concept drift study also includes fields such as
- Robustness and related Data mining, Noise, Weighting and Cohen's kappa,
- Restricted Boltzmann machine which is related to area like Robust statistics and Adversarial system.
Between 2018 and 2021, his most popular works were:
- Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation (39 citations)
- Kappa Updated Ensemble for drifting data stream mining (29 citations)
- Radial-Based oversampling for noisy imbalanced data classification (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Bartosz Krawczyk focuses on Artificial intelligence, Machine learning, Classifier, Computational complexity theory and k-nearest neighbors algorithm.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Pattern recognition.
His research integrates issues of Class and Data set in his study of Machine learning.
His Classifier research is multidisciplinary, incorporating perspectives in Training set, Imbalanced data, Radial basis function, Differential evolution and Outlier.
His Computational complexity theory study combines topics in areas such as Data stream mining and Concept drift.
His Data stream mining research integrates issues from Weighting and Robustness.
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