World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
21616
World Ranking
10427
National Ranking
16

Igor Kononenko 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 Igor Kononenko 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: 155 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Igor Kononenko 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 Igor Kononenko 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

Igor Kononenko is affiliated with the University of Ljubljana in Slovenia. Their research primarily falls within Computer Science, with a focus on several subfields including Information Systems, Artificial Intelligence, Management of Technology and Innovation, Economics and Econometrics, and Management Information Systems.

The scientist's work addresses multiple topics, notably:

  • Engineering Education and Technology
  • Information Systems and Technology Applications
  • Enterprise Management and Information Systems
  • Business and Economic Development
  • Construction Project Management and Performance
  • Economic and Technological Systems Analysis
  • Labor Market and Education

Igor Kononenko has published extensively in various scientific venues. Frequent publication venues include:

  • RADIOELECTRONIC AND COMPUTER SYSTEMS
  • Bulletin of NTU KhPI Series Strategic Management Portfolio Program and Project Management
  • Uporabna informatika
  • Journal of Business Economics and Management
  • Neural Networks

Some recent papers authored or co-authored by Igor Kononenko include:

  • "IS FISCAL POLICY ONE OF THE MOST IMPORTANT SOCIO-ECONOMIC DRIVERS FOR ENTREPRENEURIAL ACTIVITY IN EUROPEAN UNION COUNTRIES?", 2023, Journal of Business Economics and Management
  • "Mathematical model of software development project team composition optimization with fuzzy initial data", 2021, RADIOELECTRONIC AND COMPUTER SYSTEMS
  • "SuperFormer: Continual learning superposition method for text classification", 2023, Neural Networks
  • "Analysis of neuropsychological and neuroradiological features for diagnosis of Alzheimer's disease and mild cognitive impairment", 2023, International Journal of Medical Informatics
  • "ИНФОРМАЦИОННАЯ СИСТЕМА ВЫБОРА И ФОРМИРОВАНИЯ ПОДХОДА К УПРАВЛЕНИЮ ПРОЕКТОМ", 2020, RADIOELECTRONIC AND COMPUTER SYSTEMS

The scientist collaborates frequently with several researchers. Common co-authors are:

  • Zoran Bosnić
  • Hlib Sushko
  • Aleš Papič
  • Jana Faganeli Pucer
  • Maximilien Kpodjedo

Best Publications

  • Estimating attributes: analysis and extensions of RELIEF

    Igor Kononenko

  • Theoretical and Empirical Analysis of ReliefF and RReliefF

    Marko Robnik-Šikonja;Igor Kononenko

  • Explaining prediction models and individual predictions with feature contributions

    Erik Štrumbelj;Igor Kononenko

  • Machine learning for medical diagnosis: history, state of the art and perspective

    Igor Kononenko

  • Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF

    Igor Kononenko;Edvard Šimec;Marko Robnik-Šikonja

  • ASSISTANT 86: a knowledge-elicitation tool for sophisticated users

    Bojan Cestnik;Igor Kononenko;Ivan Bratko

  • An adaptation of Relief for attribute estimation in regression

    Marko Robnik-Sikonja;Igor Kononenko

  • The MONK's problems: A Performance Comparison of Different Learning Algorithms

    Sebastian B. Thrun;Jerzy W. Bala;Eric Bloedorn;Ivan Bratko

  • Semi-Naive Bayesian Classifier

    Igor Kononenko

  • Machine learning and data mining

    Igor Kononenko;Matjaž Kukar

  • An Efficient Explanation of Individual Classifications using Game Theory

    Erik Strumbelj;Igor Kononenko

  • INDUCTIVE AND BAYESIAN LEARNING IN MEDICAL DIAGNOSIS

    Igor Kononenko

  • On biases in estimating multi-valued attributes

    Igor Kononenko

  • Explaining Classifications For Individual Instances

    M. Robnik-Sikonja;I. Kononenko

  • Machine Learning and Data Mining: Introduction to Principles and Algorithms

    Igor Kononenko;Matjaz Kukar

  • Cost-Sensitive Learning with Neural Networks.

    Matjaz Kukar;Igor Kononenko

  • Information-Based Evaluation Criterion for Classifier's Performance

    Igor Kononenko;Ivan Bratko

  • Experiments in automatic learning of medical diagnostic rules

    Igor Kononenko;Ivan Bratko;E. Roskar

  • Analysing and improving the diagnosis of ischaemic heart disease with machine learning.

    Matjaž Kukar;Igor Kononenko;Ciril Grošelj;Katarina Kralj

  • Explaining instance classifications with interactions of subsets of feature values

    E. Štrumbelj;I. Kononenko;M. Robnik Šikonja

  • INDUCTION OF DECISION TREES USING RELIEFF

    I. Kononenko;E. Simec

Frequent Co-Authors

Ivan Bratko
Ivan Bratko University of Ljubljana
Nada Lavrač
Nada Lavrač Jozef Stefan Institute
João Gama
João Gama University of Porto
Aristidis Likas
Aristidis Likas University of Ioannina
Damijan Miklavčič
Damijan Miklavčič University of Ljubljana
Sebastian Thrun
Sebastian Thrun Stanford University
Kenneth de Jong
Kenneth de Jong George Mason University
Ryszard S. Michalski
Ryszard S. Michalski George Mason University

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