World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
6784
World Ranking
10630
National Ranking
665

Leandro L. Minku 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 Leandro L. Minku 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: 193 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

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

Leandro L. Minku 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 Leandro L. Minku 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Leandro L. Minku is affiliated with the University of Birmingham in the United Kingdom and specializes in the field of Computer Science, with a particular focus on Artificial Intelligence, Industrial and Manufacturing Engineering, Computer Networks and Communications, Information Systems, and Electrical and Electronic Engineering.

Their research covers diverse topics including:

  • Data Stream Mining Techniques
  • Software Engineering Research
  • Software System Performance and Reliability
  • Machine Learning and Data Classification
  • Software Reliability and Analysis Research
  • Advanced Multi-Objective Optimization Algorithms
  • Metaheuristic Optimization Algorithms Research

Recent publications by Leandro L. Minku include:

  • Surrogate models in evolutionary single-objective optimization: A new taxonomy and experimental study (2021), published in Information Sciences
  • Metaheuristics "In the Large" (2021), published in European Journal of Operational Research
  • Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model Approach (2021), published in IEEE Transactions on Knowledge and Data Engineering
  • The impact of data difficulty factors on classification of imbalanced and concept drifting data streams (2021), published in Knowledge and Information Systems
  • A Diversity Framework for Dealing With Multiple Types of Concept Drift Based on Clustering in the Model Space (2020), published in IEEE Transactions on Neural Networks and Learning Systems

Frequent co-authors collaborating with Minku include:

  • Xin Yao
  • Stefan Menzel
  • Bernhard Sendhoff
  • Liyan Song
  • Gan Ruan

Minku's work has been published extensively in a range of venues, notably:

  • IEEE Computational Intelligence Magazine, with 18 publications
  • arXiv (Cornell University), 7 publications
  • Zenodo (CERN European Organization for Nuclear Research), 6 publications
  • Empirical Software Engineering, 5 publications
  • IEEE Transactions on Neural Networks and Learning Systems, 4 publications

In addition to journal and conference papers, Minku has contributed to book publications, with recorded work published by the European Organization for Nuclear Research, including the volume titled ieee-cis/IEEE-CIS-Open-Access-Book-Volume-1: FirstEdition in 2023.

Best Publications

  • Ensemble learning for data stream analysis

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

  • The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift

    L.L. Minku;A.P. White;Xin Yao

  • DDD: A New Ensemble Approach for Dealing with Concept Drift

    L. L. Minku;Xin Yao

  • Resampling-Based Ensemble Methods for Online Class Imbalance Learning

    Shuo Wang;Leandro L. Minku;Xin Yao

  • A Systematic Study of Online Class Imbalance Learning With Concept Drift

    Shuo Wang;Leandro L. Minku;Xin Yao

  • Ensembles and locality

    Leandro L. Minku;Xin Yao

  • Online Ensemble Learning of Data Streams with Gradually Evolved Classes

    Yu Sun;Ke Tang;Leandro L. Minku;Shuo Wang

  • A learning framework for online class imbalance learning

    Shuo Wang;Leandro L. Minku;Xin Yao

  • Software effort estimation as a multiobjective learning problem

    Leandro L. Minku;Xin Yao

  • Next challenges for adaptive learning systems

    Indre Zliobaite;Albert Bifet;Mohamed Gaber;Bogdan Gabrys

  • A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling

    Xiao-Ning Shen;Leandro L. Minku;Naresh Marturi;Yi-Nan Guo

  • Concept drift detection for online class imbalance learning

    Shuo Wang;Leandro L. Minku;Davide Ghezzi;Daniele Caltabiano

  • An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation

    Mohammad Azzeh;Ali Bou Nassif;Leandro L. Minku

  • Surrogate models in evolutionary single-objective optimization: A new taxonomy and experimental study

    Hao Tong;Changwu Huang;Leandro L. Minku;Xin Yao

  • The impact of parameter tuning on software effort estimation using learning machines

    Liyan Song;Leandro L. Minku;Xin Yao

  • Class imbalance evolution and verification latency in just-in-time software defect prediction

    George G. Cabral;Leandro L. Minku;Emad Shihab;Suhaib Mujahid

  • ONLINE CLASS IMBALANCE LEARNING AND ITS APPLICATIONS IN FAULT DETECTION

    Shuo Wang;Leandro L. Minku;Xin Yao

  • Metaheuristics “In the Large”

    Jerry Swan;Steven Adriaensen;Alexander E.I. Brownlee;Kevin Hammond

  • How to make best use of cross-company data in software effort estimation?

    Leandro L. Minku;Xin Yao

  • Dynamic Software Project Scheduling through a Proactive-Rescheduling Method

    Xiaoning Shen;Leandro L. Minku;Rami Bahsoon;Xin Yao

  • Sharing Data and Models in Software Engineering: Sharing Data and Models

    Tim Menzies;Ekrem Kocaguneli;Leandro L. Minku;Fayola Peters

Frequent Co-Authors

Xin Yao
Xin Yao Lingnan University
Tim Menzies
Tim Menzies North Carolina State University
Burak Turhan
Burak Turhan Monash University
Ayse Bener
Ayse Bener Toronto Metropolitan University
Rami Bahsoon
Rami Bahsoon University of Birmingham
Bernhard Sendhoff
Bernhard Sendhoff Honda (Germany)
Emilia Mendes
Emilia Mendes Aarhus University
Markus Wagner
Markus Wagner Monash University
Nitesh V. Chawla
Nitesh V. Chawla University of Notre Dame
Rick Kazman
Rick Kazman University of Hawaii at Manoa

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