World's Best Scientists 2026 revealed!
Nathalie Japkowicz

Nathalie Japkowicz

D-Index & Metrics

Computer Science

D-Index
45
Citations
24450
World Ranking
6975
National Ranking
3054

Nathalie Japkowicz 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 Nathalie Japkowicz 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: 214 publications — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Nathalie Japkowicz 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 Nathalie Japkowicz 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Nathalie Japkowicz is affiliated with American University in the United States and has contributed extensively to the field of Computer Science, with a focus on Artificial Intelligence. Their research spans multiple subfields including Signal Processing, Sociology and Political Science, Radiology, Nuclear Medicine and Imaging, and Computer Networks and Communications.

The scientist has published significant work on topics such as anomaly detection techniques and applications, imbalanced data classification techniques, misinformation and its impacts, network security and intrusion detection, COVID-19 diagnosis using AI, hate speech and cyberbullying detection, and data stream mining techniques.

Recent notable papers include:

  • "The class imbalance problem in deep learning" (2022) published in Machine Learning
  • "Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods" (2023) published in IEEE Access
  • "On the joint-effect of class imbalance and overlap: a critical review" (2022) published in Artificial Intelligence Review
  • "A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research" (2022) published in Information Fusion
  • "Research on unsupervised feature learning for Android malware detection based on Restricted Boltzmann Machines" (2021) published in Future Generation Computer Systems

Japkowicz has frequently collaborated with several researchers, including Roberto Corizzo, Evan Crothers, Zois Boukouvalas, Kamil Faber, and Herna L. Viktor.

Their work has appeared in various publication venues with multiple contributions to:

  • arXiv (Cornell University)
  • Machine Learning
  • IEEE Access
  • 2021 IEEE International Conference on Big Data (Big Data)
  • 2022 International Joint Conference on Neural Networks (IJCNN)

Japkowicz has also authored a book titled Machine Learning Evaluation, published in 2024 by Cambridge University Press.

Best Publications

  • The class imbalance problem: A systematic study

    Nathalie Japkowicz;Shaju Stephen

  • Editorial: special issue on learning from imbalanced data sets

    Nitesh V. Chawla;Nathalie Japkowicz;Aleksander Kotcz

  • SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS

    N Chawla;N Japkowicz;A Kolcz

  • Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation

    Marina Sokolova;Nathalie Japkowicz;Stan Szpakowicz

  • Applying support vector machines to imbalanced datasets

    Rehan Akbani;Stephen Kwek;Nathalie Japkowicz

  • A Multiple Resampling Method for Learning from Imbalanced Data Sets

    Andrew Estabrooks;Taeho Jo;Nathalie Japkowicz

  • Evaluating Learning Algorithms: A Classification Perspective

    Nathalie Japkowicz;Mohak Shah

  • Class imbalances versus small disjuncts

    Taeho Jo;Nathalie Japkowicz

  • Learning from Imbalanced Data Sets: A Comparison of Various Strategies *

    Nathalie Japkowicz

  • A novelty detection approach to classification

    Nathalie Japkowicz;Catherine Myers;Mark Gluck

  • Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks

    Adrian Taylor;Sylvain Leblanc;Nathalie Japkowicz

  • Boosting support vector machines for imbalanced data sets

    Benjamin X. Wang;Nathalie Japkowicz

  • Evaluating Learning Algorithms: Contents

    Unknown

  • Frequency-based anomaly detection for the automotive CAN bus

    Adrian Taylor;Nathalie Japkowicz;Sylvain Leblanc

  • Nonlinear Autoassociation Is Not Equivalent to PCA

    Nathalie Japkowicz;Stephen Jose Hanson;Mark A. Gluck

  • Concept-Learning in the Presence of Between-Class and Within-Class Imbalances

    Nathalie Japkowicz

  • Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks

    Nathalie Japkowicz

  • Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

    Unknown

  • A Feature Selection and Evaluation Scheme for Computer Virus Detection

    Olivier Henchiri;Nathalie Japkowicz

  • Concept learning in the absence of counterexamples: an autoassociation-based approach to classification

    Nathalie Japkowicz;Jose Hanson;Casimir Kulikowski

  • A Mixture-of-Experts Framework for Learning from Imbalanced Data Sets

    Andrew Estabrooks;Nathalie Japkowicz

  • Canadian Conference on Artificial Intelligence

    William Klement;Peter A Flach;Nathalie Japkowicz;Stan Matwin

  • Privacy-preserving collaborative association rule mining

    Justin Zhan;Stan Matwin;LiWu Chang

Frequent Co-Authors

Stan Matwin
Stan Matwin Dalhousie University
Michelangelo Ceci
Michelangelo Ceci University of Bari Aldo Moro
Bartosz Krawczyk
Bartosz Krawczyk Rochester Institute of Technology
Stan Szpakowicz
Stan Szpakowicz University of Ottawa
Osmar R. Zaïane
Osmar R. Zaïane University of Alberta
Evangelos E. Milios
Evangelos E. Milios Dalhousie University
Peter A. Flach
Peter A. Flach University of Bristol
Jerzy Stefanowski
Jerzy Stefanowski Poznań University of Technology
Mark A. Gluck
Mark A. Gluck Rutgers, The State University of New Jersey
Tulay Adali
Tulay Adali University of Maryland, Baltimore County

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