World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
12700
World Ranking
3116
National Ranking
418

Lean Yu 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 Lean Yu 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: 252 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Lean Yu 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 Lean Yu 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: 61 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Overview

Lean Yu is affiliated with the Academy of Mathematics and Systems Science in China and has contributed extensively to research in computer science and engineering. Their work covers a broad range of topics with a significant focus on management science, operations research, and artificial intelligence.

Their research portfolio includes studies in imbalanced data classification techniques, financial distress and bankruptcy prediction, energy load and power forecasting, market dynamics and volatility, stock market forecasting methods, grey system theory applications, and forecasting techniques and applications.

Lean Yu has published extensively in leading venues, with frequent contributions to Expert Systems with Applications, Information Sciences, INFORMS Journal on Computing, Applied Soft Computing, and Procedia Computer Science.

Their recent papers include:

  • "Missing Data Preprocessing in Credit Classification: One-Hot Encoding or Imputation?" (2020) in Emerging Markets Finance and Trade
  • "Trajectory prediction for heterogeneous traffic-agents using knowledge correction data-driven model" (2022) in Information Sciences
  • "Blockchain-driven supply chain finance solution for small and medium enterprises" (2020) in Frontiers of Engineering Management
  • "An effective rolling decomposition-ensemble model for gasoline consumption forecasting" (2021) in Energy
  • "Decarbonizing China's power sector by 2030 with consideration of technological progress and cross-regional power transmission" (2021) in Energy Policy

Frequent co-authors collaborating with Lean Yu include Xiaoming Zhang, Hang Yin, Yixiang Ma, Guoxing Zhang, and Xi Xi.

The main fields of study addressed by Lean Yu encompass:

  • Computer Science
  • Engineering

Within these fields, notable subfields of study are:

  • Management Science and Operations Research
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Economics and Econometrics
  • Accounting

Best Publications

  • Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

    Lean Yu;Shouyang Wang;Kin Keung Lai

  • Credit risk assessment with a multistage neural network ensemble learning approach

    Lean Yu;Shouyang Wang;Kin Keung Lai

  • A deep learning ensemble approach for crude oil price forecasting

    Yang Zhao;Jianping Li;Lean Yu

  • A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

    Lean Yu;Shouyang Wang;K.K. Lai

  • A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support

    Lean Yu;Kin Keung Lai

  • A new method for crude oil price forecasting based on support vector machines

    Wen Xie;Lean Yu;Shanying Xu;Shouyang Wang

  • Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method

    Xun Zhang;Lean Yu;Shouyang Wang;Kin Keung Lai

  • An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

    Lean Yu;Lean Yu;Shouyang Wang;Kin Keung Lai

  • Evolving Least Squares Support Vector Machines for Stock Market Trend Mining

    Lean Yu;Huanhuan Chen;Shouyang Wang;Kin Keung Lai

  • Online big data-driven oil consumption forecasting with Google trends

    Lean Yu;Yaqing Zhao;Ling Tang;Zebin Yang

  • A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting

    Lean Yu;Wei Dai;Ling Tang

  • A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

    Lean Yu;Zishu Wang;Ling Tang

  • Multistage RBF neural network ensemble learning for exchange rates forecasting

    Lean Yu;Kin Keung Lai;Shouyang Wang

  • Support vector machine based multiagent ensemble learning for credit risk evaluation

    Lean Yu;Wuyi Yue;Shouyang Wang;K. K. Lai

  • Least squares support vector machines ensemble models for credit scoring

    Ligang Zhou;Kin Keung Lai;Lean Yu

  • An integrated data preparation scheme for neural network data analysis

    Lean Yu;Shouyang Wang;K.K. Lai

  • A neural-network-based nonlinear metamodeling approach to financial time series forecasting

    Lean Yu;Shouyang Wang;Kin Keung Lai

  • A non-iterative decomposition-ensemble learning paradigm using RVFL network for crude oil price forecasting

    Ling Tang;Ling Tang;Yao Wu;Lean Yu

  • A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

    Ling Tang;Lean Yu;Lean Yu;Shuai Wang;Jianping Li

  • NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING

    Wei Huang;Wei Huang;Kin Keung Lai;Kin Keung Lai;Yoshiteru Nakamori;Shouyang Wang;Shouyang Wang

  • Neural network-based mean-variance-skewness model for portfolio selection

    Lean Yu;Shouyang Wang;Kin Keung Lai

Frequent Co-Authors

Shouyang Wang
Shouyang Wang Chinese Academy of Sciences
Kin Keung Lai
Kin Keung Lai Shaanxi Normal University
Wai-Ki Ching
Wai-Ki Ching University of Hong Kong
Yukun Bao
Yukun Bao Huazhong University of Science and Technology
Gang Kou
Gang Kou Southwestern University of Finance and Economics
Aoying Zhou
Aoying Zhou East China Normal University
Yong Shi
Yong Shi Chinese Academy of Sciences
Fenghua Wen
Fenghua Wen Central South University
Enrique Herrera-Viedma
Enrique Herrera-Viedma University of Granada
Masao Fukushima
Masao Fukushima Kyoto University

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