World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
13469
World Ranking
6054
National Ranking
48

Chih-Fong Tsai 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 Chih-Fong Tsai 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 154 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

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

Chih-Fong Tsai 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 Chih-Fong Tsai sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Overview

Chih-Fong Tsai is affiliated with National Central University in Taiwan and has a significant body of research primarily within the field of Computer Science. Their work prominently covers several subfields, including Artificial Intelligence, Health Information Management, Accounting, Computer Vision and Pattern Recognition, and Management Science and Operations Research.

The research topics that Tsai has contributed to include:

  • Imbalanced Data Classification Techniques
  • Artificial Intelligence in Healthcare
  • Financial Distress and Bankruptcy Prediction
  • Machine Learning and Data Classification
  • Sentiment Analysis and Opinion Mining
  • Digital Marketing and Social Media
  • Neural Networks and Applications

Frequent co-authors in Tsai's publications include Wei-Chao Lin, Ya-Han Hu, Min-Wei Huang, Deron Liang, and Kuen-Liang Sue.

Tsai's research has been published across various journals, with notable frequent publication venues being:

  • Applied Sciences
  • Journal of Experimental & Theoretical Artificial Intelligence
  • Journal of Business Research
  • Knowledge-Based Systems
  • Expert Systems with Applications

Some of the recent papers authored or co-authored by Tsai are:

  • Improving text summarization of online hotel reviews with review helpfulness and sentiment, 2020, Tourism Management
  • Ensemble feature selection in high dimension, low sample size datasets: Parallel and serial combination approaches, 2020, Knowledge-Based Systems
  • Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction, 2021, Journal of Business Research

These papers address various aspects of machine learning, data classification, and financial distress prediction, contributing to knowledge in both artificial intelligence applications and business research contexts.

Best Publications

  • Review: Intrusion detection by machine learning: A review

    Chih-Fong Tsai;Yu-Feng Hsu;Chia-Ying Lin;Wei-Yang Lin

  • Clustering-based undersampling in class-imbalanced data

    Wei Chao Lin;Chih Fong Tsai;Ya Han Hu;Jing Shang Jhang

  • Using neural network ensembles for bankruptcy prediction and credit scoring

    Chih-Fong Tsai;Jhen-Wei Wu

  • CANN: An intrusion detection system based on combining cluster centers and nearest neighbors

    Wei Chao Lin;Shih Wen Ke;Chih Fong Tsai

  • The distance function effect on k-nearest neighbor classification for medical datasets

    Li Yu Hu;Min Wei Huang;Shih Wen Ke;Chih Fong Tsai

  • Missing value imputation: a review and analysis of the literature (2006–2017)

    Unknown

  • Machine learning in concrete strength simulations: Multi-nation data analytics

    Jui Sheng Chou;Chih Fong Tsai;Anh Duc Pham;Anh Duc Pham;Yu Hsin Lu

  • Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches

    Chih-Fong Tsai;Yu-Chieh Hsiao

  • Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study

    Deron Liang;Chia Chi Lu;Chih Fong Tsai;Guan An Shih

  • Machine Learning in Financial Crisis Prediction: A Survey

    Wei-Yang Lin;Ya-Han Hu;Chih-Fong Tsai

  • Customer churn prediction by hybrid neural networks

    Chih-Fong Tsai;Yu-Hsin Lu

  • SVM and SVM Ensembles in Breast Cancer Prediction.

    Min Wei Huang;Chih Wen Chen;Wei Chao Lin;Shih Wen Ke

  • A triangle area based nearest neighbors approach to intrusion detection

    Chih-Fong Tsai;Chia-Ying Lin

  • Feature selection in bankruptcy prediction

    Chih-Fong Tsai

  • Under-sampling class imbalanced datasets by combining clustering analysis and instance selection

    Chih Fong Tsai;Wei Chao Lin;Wei Chao Lin;Ya Han Hu;Guan Ting Yao

  • Genetic algorithms in feature and instance selection

    Chih-Fong Tsai;William Eberle;Chi-Yuan Chu

  • The effect of feature selection on financial distress prediction

    Deron Liang;Chih-Fong Tsai;Hsin-Ting Wu

  • Predicting stock returns by classifier ensembles

    Chih-Fong Tsai;Yuah-Chiao Lin;David C. Yen;Yan-Min Chen

  • A comparative study of classifier ensembles for bankruptcy prediction

    Chih-Fong Tsai;Yu-Feng Hsu;David C. Yen

  • Bag-of-Words Representation in Image Annotation: A Review

    Unknown

  • Combining cluster analysis with classifier ensembles to predict financial distress

    Chih-Fong Tsai

  • Credit rating by hybrid machine learning techniques

    Chih-Fong Tsai;Ming-Lun Chen

Frequent Co-Authors

Jui-Sheng Chou
Jui-Sheng Chou National Taiwan University of Science and Technology
David C. Yen
David C. Yen Texas Southern University

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