World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
12301
World Ranking
8179
National Ranking
3506

Bartosz Krawczyk publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Bartosz Krawczyk sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 205 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Bartosz Krawczyk D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Bartosz Krawczyk sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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 Cano
Alberto Cano Virginia Commonwealth University
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

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