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.
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.
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
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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Learning from imbalanced data: open challenges and future directions
Progress in Artificial Intelligence (2016)
Ensemble learning for data stream analysis
Bartosz Krawczyk;Leandro L. Minku;Joo Gama;Jerzy Stefanowski.
Information Fusion (2017)
A survey on data preprocessing for data stream mining
Sergio Ramrez-Gallego;Bartosz Krawczyk;Salvador Garca;Micha Woniak.
Cost-sensitive decision tree ensembles for effective imbalanced classification
Bartosz Krawczyk;Michał Woniak;Gerald Schaefer.
soft computing (2014)
Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy
Bartosz Krawczyk;Mikel Galar;Łukasz Jeleń;Francisco Herrera.
soft computing (2016)
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets
José A. Sáez;Bartosz Krawczyk;Michał Woźniak.
Pattern Recognition (2016)
Clustering-based ensembles for one-class classification
Bartosz Krawczyk;Michał Woniak;Bogusław Cyganek.
Information Sciences (2014)
An ensemble classification approach for melanoma diagnosis
Gerald Schaefer;Bartosz Krawczyk;M. Emre Celebi;Hitoshi Iyatomi.
Memetic Computing (2014)
Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data
Zhongliang Zhang;Bartosz Krawczyk;Salvador Garcìa;Alejandro Rosales-Pérez.
Knowledge Based Systems (2016)
One-class classifiers with incremental learning and forgetting for data streams with concept drift
Bartosz Krawczyk;Michał Woźniak.
soft computing (2015)
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