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Charles X. Ling

D-Index & Metrics

Engineering and Technology

D-Index
38
Citations
11389
World Ranking
7887
National Ranking
318

Charles X. Ling publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Charles X. Ling sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 168 publications — 35th percentile

35% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Charles X. Ling D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Charles X. Ling sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 38 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Research.com Recognitions

  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering

Overview

Charles X. Ling is affiliated with the University of Western Ontario in Canada. Their research primarily focuses on the field of Computer Science, with particular emphasis on Artificial Intelligence, Computer Vision and Pattern Recognition, and related subfields. They have a significant number of publications that contribute to various areas of machine learning and neural networks.

The main topics covered in their work include:

  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Machine Learning and Data Classification
  • Anomaly Detection Techniques and Applications
  • Topic Modeling
  • Privacy-Preserving Technologies in Data
  • Advanced Neural Network Applications

Among their recent papers are the following:

  • "When Source-Free Domain Adaptation Meets Learning with Noisy Labels" (2023), arXiv (Cornell University)
  • "Ensemble Learning With Attention-Integrated Convolutional Recurrent Neural Network for Imbalanced Speech Emotion Recognition" (2020), IEEE Access
  • "On Learning Fairness and Accuracy on Multiple Subgroups" (2022), arXiv (Cornell University)
  • "Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation" (2023), Proceedings of the AAAI Conference on Artificial Intelligence
  • "Episodic task agnostic contrastive training for multi-task learning" (2023), Neural Networks

Frequent co-authors who have collaborated extensively with Charles X. Ling include:

  • Boyu Wang
  • Ruizhi Pu
  • Changjian Shui
  • Gezheng Xu

The venues where the researcher has frequently published are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Neural Networks
  • IEEE Transactions on Knowledge and Data Engineering
  • Scientific Reports

Charles X. Ling has also been recognized by The Canadian Academy of Engineering.

Best Publications

  • Using AUC and accuracy in evaluating learning algorithms

    Jin Huang;C.X. Ling

  • Data mining for direct marketing: problems and solutions

    Charles X. Ling;Chenghui Li

  • AUC: a statistically consistent and more discriminating measure than accuracy

    Charles X. Ling;Jin Huang;Harry Zhang

  • AUC: a better measure than accuracy in comparing learning algorithms

    Charles X. Ling;Jin Huang;Harry Zhang

  • DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

    Wenyin Gong;Zhihua Cai;Charles X. Ling

  • Decision trees with minimal costs

    Charles X. Ling;Qiang Yang;Jianning Wang;Shichao Zhang

  • Pelee: a real-time object detection system on mobile devices

    Robert J. Wang;Xiang Li;Charles X. Ling

  • Comparing naive Bayes, decision trees, and SVM with AUC and accuracy

    J. Huang;J. Lu;C.X. Ling

  • Cost-Sensitive Learning and the Class Imbalance Problem

    Charles X. Ling;Victor S. Sheng

  • Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization

    Wenyin Gong;Zhihua Cai;Charles X Ling;Hui Li

  • "Missing is useful": missing values in cost-sensitive decision trees

    Shichao Zhang;Z. Qin;C.X. Ling;S. Sheng

  • Answering the connectionist challenge: a symbolic model of learning the past tenses of English verbs

    Charles X. Ling;Marin Marinov

  • Test-cost sensitive naive Bayes classification

    Xiaoyong Chai;Lin Deng;Qiang Yang;C.X. Ling

  • Thresholding for making classifiers cost-sensitive

    Victor S. Sheng;Charles X. Ling

  • Test strategies for cost-sensitive decision trees

    C.X. Ling;V.S. Sheng;Q. Yang

  • A real-coded biogeography-based optimization with mutation

    Wenyin Gong;Zhihua Cai;Charles X. Ling;Hui Li

  • pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry

    Dequan Li;Yan Fu;Ruixiang Sun;Charles X. Ling

  • Discriminative parameter learning for Bayesian networks

    Jiang Su;Harry Zhang;Charles X. Ling;Stan Matwin

  • A clustering-based differential evolution for global optimization

    Zhihua Cai;Wenyin Gong;Charles X. Ling;Harry Zhang

  • Reviewer Recommender of Pull-Requests in GitHub

    Yue Yu;Huaimin Wang;Gang Yin;Charles X. Ling

  • Cost-Sensitive Learning.

    Charles X. Ling;Victor S. Sheng

  • Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry

    Yan Fu;Qiang Yang;Ruixiang Sun;Dequan Li

  • Extracting Actionable Knowledge from Decision Trees

    Qiang Yang;Jie Yin;C. Ling;Rong Pan

  • Keyphrase Extraction Using Semantic Networks Structure Analysis

    Chong Huang;Yonghong Tian;Zhi Zhou;C.X. Ling

  • Postprocessing decision trees to extract actionable knowledge

    Qiang Yang;Jie Yin;C.X. Ling;T. Chen

  • Test-cost sensitive classification on data with missing values

    Qiang Yang;C. Ling;X. Chai;Rong Pan

  • Simple test strategies for cost-sensitive decision trees

    Shengli Sheng;Charles X. Ling;Qiang Yang

  • Machine learning for stock selection

    Robert J. Yan;Charles X. Ling

Frequent Co-Authors

Victor S. Sheng
Victor S. Sheng Texas Tech University
Qiang Yang
Qiang Yang Hong Kong University of Science and Technology
Wen Gao
Wen Gao Peking University
Stan Matwin
Stan Matwin Dalhousie University
Wenyin Gong
Wenyin Gong China University of Geosciences
Yiqiang Chen
Yiqiang Chen Chinese Academy of Sciences
Wai Lam
Wai Lam Chinese University of Hong Kong
Jinhua Zheng
Jinhua Zheng Xiangtan University
David W. Aha
David W. Aha United States Naval Research Laboratory
Rong Zeng
Rong Zeng Chinese Academy of Sciences

External Links

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Best Scientists Citing Charles X. Ling

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Chengqi Zhang

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Liangxiao Jiang

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Sotiris Kotsiantis

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Yiyu Yao

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Zhihua Cai

Zhihua Cai

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Publications: 20

Qiang Yang

Qiang Yang

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Publications: 18

Longbing Cao

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Publications: 15

Stan Matwin

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Nathalie Japkowicz

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