Min-Ling Zhang

15.6k total citations · 6 hit papers
177 papers, 10.8k citations indexed

About

Min-Ling Zhang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Min-Ling Zhang has authored 177 papers receiving a total of 10.8k indexed citations (citations by other indexed papers that have themselves been cited), including 116 papers in Artificial Intelligence, 70 papers in Computer Vision and Pattern Recognition and 25 papers in Information Systems. Recurrent topics in Min-Ling Zhang's work include Text and Document Classification Technologies (93 papers), Image Retrieval and Classification Techniques (41 papers) and Machine Learning and Data Classification (32 papers). Min-Ling Zhang is often cited by papers focused on Text and Document Classification Technologies (93 papers), Image Retrieval and Classification Techniques (41 papers) and Machine Learning and Data Classification (32 papers). Min-Ling Zhang collaborates with scholars based in China, United States and Chile. Min-Ling Zhang's co-authors include Zhi‐Hua Zhou, Lei Wu, Kun Zhang, Xin Geng, José M. Peña, Vı́ctor Robles, F. Richard Yu, Xuying Liu, Yukun Li and Sheng-Jun Huang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and Journal of Membrane Science.

In The Last Decade

Min-Ling Zhang

158 papers receiving 10.4k citations

Hit Papers

ML-KNN: A lazy learning approach to multi-label learning 2006 2026 2012 2019 2007 2013 2006 2014 2009 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. ML-KNN: A lazy learning approach to multi-label learning
    2007 2.5k citations Min-Ling Zhang, Zhi‐Hua Zhou Pattern Recognition profile →
  2. A Review on Multi-Label Learning Algorithms
    2013 2.2k citations Min-Ling Zhang, Zhi‐Hua Zhou profile →
  3. Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
    2006 829 citations Min-Ling Zhang, Zhi‐Hua Zhou profile →
  4. Lift: Multi-Label Learning with Label-Specific Features
    2014 406 citations Min-Ling Zhang et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
  5. Feature selection for multi-label naive Bayes classification
    2009 397 citations Min-Ling Zhang et al. profile →
  6. Binary relevance for multi-label learning: an overview
    2017 276 citations Min-Ling Zhang et al. profile →

Peers

Min-Ling Zhang
Cho‐Jui Hsieh United States
Shichao Zhang China
George H. John United States
Manoranjan Dash Singapore
Kai‐Wei Chang Taiwan
Grigorios Tsoumakas Greece
Kyunghyun Cho United States
Alex A. Freitas United Kingdom
Shuiwang Ji United States
Cho‐Jui Hsieh United States
Min-Ling Zhang
Citations per year, relative to Min-Ling Zhang Min-Ling Zhang (= 1×) peers Cho‐Jui Hsieh

Countries citing papers authored by Min-Ling Zhang

Since Specialization
Citations

This map shows the geographic impact of Min-Ling Zhang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Min-Ling Zhang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min-Ling Zhang more than expected).

Fields of papers citing papers by Min-Ling Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Min-Ling Zhang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Min-Ling Zhang. The network helps show where Min-Ling Zhang may publish in the future.

Co-authorship network of co-authors of Min-Ling Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Min-Ling Zhang. A scholar is included among the top collaborators of Min-Ling Zhang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Min-Ling Zhang. Min-Ling Zhang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Xue, Hui, et al.. (2025). Toward Few-Shot Learning in the Open World: A Review and Beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(11). 10420–10440.
2.
Cao, Xiaofeng, Xin Yu, Jiangchao Yao, et al.. (2025). Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation. ACM Computing Surveys. 58(6). 1–47.
3.
Tang, Xiao, et al.. (2025). ZzzMate: Designing an Empathetic Chatbot for Addressing Self-Conscious Emotions for Sleep Adherence. International Journal of Human-Computer Interaction. 1–26.
4.
Chen, Miaomiao, Min-Ling Zhang, Jun Jiang, et al.. (2024). Smartphone-assisted electrochemiluminescence imaging test strips towards dual-signal visualized and sensitive monitoring of aflatoxin B1 in corn samples. Chinese Chemical Letters. 36(1). 109785–109785. 11 indexed citations
5.
Zhang, Jian, Tong Wei, & Min-Ling Zhang. (2024). Label-Specific Time–Frequency Energy-Based Neural Network for Instrument Recognition. IEEE Transactions on Cybernetics. 54(11). 7080–7093. 2 indexed citations
6.
Zhang, Shuo, Jianqing Li, Hamido Fujita, et al.. (2024). Student Loss: Towards the Probability Assumption in Inaccurate Supervision. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(6). 4460–4475.
7.
Gao, Yi, Miao Xu, & Min-Ling Zhang. (2024). Complementary to Multiple Labels: A Correlation-Aware Correction Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 9179–9191. 1 indexed citations
8.
Zhang, Yu, et al.. (2023). Temporal segment dropout for human action video recognition. Pattern Recognition. 146. 109985–109985. 10 indexed citations
9.
Du, Yuzhe, Yu Cheng Zhu, Maribel Portilla, Min-Ling Zhang, & Gadi V. P. Reddy. (2023). The mechanisms of metabolic resistance to pyrethroids and neonicotinoids fade away without selection pressure in the tarnished plant bug Lygus lineolaris. Pest Management Science. 79(10). 3893–3902. 10 indexed citations
10.
Charlton, Nikki D., Mihwa Yi, Clive H. Bock, Min-Ling Zhang, & Carolyn A. Young. (2020). First description of the sexual stage of Venturia effusa , causal agent of pecan scab. Mycologia. 112(4). 711–721. 12 indexed citations
11.
Bock, Clive H., Carolyn A. Young, Min-Ling Zhang, et al.. (2020). Mating Type Idiomorphs, Heterothallism, and High Genetic Diversity in Venturia carpophila, Cause of Peach Scab. Phytopathology. 111(2). 408–424. 5 indexed citations
12.
Wang, Wei & Min-Ling Zhang. (2020). Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization. Neural Information Processing Systems. 33. 6982–6993. 9 indexed citations
13.
Jimbo, Masahito, Ananda Sen, Melissa Plegue, et al.. (2019). Interactivity in a Decision Aid: Findings From a Decision Aid to Technologically Enhance Shared Decision Making RCT. American Journal of Preventive Medicine. 57(1). 77–86. 6 indexed citations
14.
Zhang, Min-Ling & F. Richard Yu. (2015). Solving the partial label learning problem: an instance-based approach. International Conference on Artificial Intelligence. 4048–4054. 77 indexed citations
15.
Yu, F. Richard & Min-Ling Zhang. (2015). Maximum Margin Partial Label Learning. Asian Conference on Machine Learning. 96–111. 7 indexed citations
16.
Zhang, Min-Ling & Zhi‐Hua Zhou. (2009). Classifier Ensemble with Unlabeled Data. arXiv (Cornell University). 2 indexed citations
17.
Zhang, Min-Ling & Zhi‐Hua Zhou. (2007). Multi-label learning by instance differentiation. National Conference on Artificial Intelligence. 669–674. 52 indexed citations
18.
Zhang, Min-Ling. (1999). THE INFLUENCE OF THE THICKNESS OF HOST EGGS CHORION ON THE LONGEVITY AND FECUNDITY OF TRICHOGRAMMA CONFUSUM.
19.
Zhang, Min-Ling. (1997). Effects of 14 insecticides on adults, larvae, eggs, and pupae of Trichogramma confusum.. 19(1). 11–14. 3 indexed citations
20.
Zhang, Min-Ling, et al.. (1996). Rearing of Eocanthecona furcellata. 18(2). 74–77. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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