World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
12301
World Ranking
8180
National Ranking
3506

Overview

Bartosz Krawczyk is affiliated with the Rochester Institute of Technology in the United States. The research primarily focuses on computer science, with significant contributions in the subfields of artificial intelligence, electrical and electronic engineering, computer vision and pattern recognition, management science and operations research, and health information management.

The scientist's work covers multiple topics within these areas, including:

  • Data Stream Mining Techniques
  • Imbalanced Data Classification Techniques
  • Anomaly Detection Techniques and Applications
  • Machine Learning and Data Classification
  • Electricity Theft Detection Techniques
  • Advanced Bandit Algorithms Research
  • Artificial Intelligence in Healthcare

Most publications appear under the computer science domain, with frequent publications in venues such as:

  • arXiv (Cornell University)
  • Machine Learning
  • Knowledge-Based Systems
  • IEEE Transactions on Neural Networks and Learning Systems
  • Neurocomputing

Recent notable papers include:

  • "DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data" (2022), published in IEEE Transactions on Neural Networks and Learning Systems
  • "The class imbalance problem in deep learning" (2022), published in Machine Learning
  • "A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework" (2023), published in Machine Learning
  • "ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams" (2022), published in Machine Learning
  • "Multi-class imbalanced big data classification on Spark" (2020), published in Knowledge-Based Systems

The scientist frequently collaborates with several co-authors, including:

  • Łukasz Korycki
  • Nitesh V. Chawla
  • Damien Dablain
  • Colin Bellinger
  • Alberto Cano

Best Publications

  • Learning from imbalanced data: open challenges and future directions

    Bartosz Krawczyk

  • Ensemble learning for data stream analysis

    Bartosz Krawczyk;Leandro L. Minku;Joo Gama;Jerzy Stefanowski

  • A survey on data preprocessing for data stream mining

    Sergio Ramrez-Gallego;Bartosz Krawczyk;Salvador Garca;Micha Woniak

  • DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.

    Damien Dablain;Bartosz Krawczyk;Nitesh V. Chawla

  • Cost-sensitive decision tree ensembles for effective imbalanced classification

    Bartosz Krawczyk;Michał Woniak;Gerald Schaefer

  • Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy

    Bartosz Krawczyk;Mikel Galar;Łukasz Jeleń;Francisco Herrera

  • Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets

    José A. Sáez;Bartosz Krawczyk;Michał Woźniak

  • Kappa Updated Ensemble for drifting data stream mining

    Alberto Cano;Bartosz Krawczyk

  • Clustering-based ensembles for one-class classification

    Bartosz Krawczyk;Michał Woniak;Bogusław Cyganek

  • Radial-Based oversampling for noisy imbalanced data classification

    Michał Koziarski;Bartosz Krawczyk;Michał Woźniak

  • ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams

    Unknown

  • Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation

    Anabel Gómez-Ríos;Siham Tabik;Julián Luengo;A. S. M. Shihavuddin

  • Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data

    Zhongliang Zhang;Bartosz Krawczyk;Salvador Garcìa;Alejandro Rosales-Pérez

  • An ensemble classification approach for melanoma diagnosis

    Gerald Schaefer;Bartosz Krawczyk;M. Emre Celebi;Hitoshi Iyatomi

  • Combined Cleaning and Resampling algorithm for multi-class imbalanced data with label noise

    Michał Koziarski;Michał Woźniak;Bartosz Krawczyk

  • Radial-Based Oversampling for Multiclass Imbalanced Data Classification

    Bartosz Krawczyk;Michal Koziarski;Michal Wozniak

  • Online ensemble learning with abstaining classifiers for drifting and noisy data streams

    Bartosz Krawczyk;Alberto Cano

  • One-class classifiers with incremental learning and forgetting for data streams with concept drift

    Bartosz Krawczyk;Michał Woźniak

  • Dynamic ensemble selection for multi-class classification with one-class classifiers

    Bartosz Krawczyk;Mikel Galar;Michał Woźniak;Humberto Bustince

  • Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark

    Sergio Ramirez-Gallego;Bartosz Krawczyk;Salvador Garcia;Michal Wozniak

  • Diversity measures for one-class classifier ensembles

    Bartosz Krawczyk;Michał Woniak

Frequent Co-Authors

Michal Wozniak
Michal Wozniak Wrocław University of Science and Technology
Francisco Herrera
Francisco Herrera University of Granada
Gerald Schaefer
Gerald Schaefer Loughborough University
Mikel Galar
Mikel Galar Universidad Publica De Navarra
Alberto Fernández
Alberto Fernández University of Granada
Salvador García
Salvador García University of Granada
Nathalie Japkowicz
Nathalie Japkowicz American University
Julián Luengo
Julián Luengo University of Granada
M. Emre Celebi
M. Emre Celebi University of Central Arkansas
Jerzy Stefanowski
Jerzy Stefanowski Poznań University of Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Considering expanding your studies in computer science? Exploring online degrees offers flexibility, affordability, and varied career options. For those aiming for advanced education quickly, the quickest cheapest masters degree programs are a smart choice. These fast-tracked, budget-friendly master's options can help boost your qualifications without a long time commitment.

Not sure which graduate program to choose? Researching what masters program should i do will help you understand which in-demand master's degrees can open doors to rewarding career opportunities in tech and beyond.

If you’re just starting your journey in computer science, online associate degree programs are accessible entry points that provide foundational knowledge and skills for the field. For budget-conscious students, exploring the cheapest online colleges can lead to significant savings without sacrificing educational quality.

Whether you’re seeking foundational credentials or advanced expertise, online study pathways make it easier to tailor your education to your career goals and budget.

Best Scientists Citing Bartosz Krawczyk

Trending Scientists