PROFILE
Zhou Peng is an Associate Professor with the School of Computer Science and Technology at Anhui University. He received his Ph.D degree in computer science from Hefei University of Technology, China. His research interests include data mining and machine learning, stream learning, feature selection, anomaly detection and explainable machine learning.
Google Scholar: https://scholar.google.com/citations?user=IZCki_cAAAAJ&hl=en
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RECENT POSTS
New paper is coming!
Peng Zhou, Huiqi Deng, Yunyun Zhang, Zhaolong Ling, Xindong Wu. Online Distributed Heterogeneous Streaming Feature Selection, ACM Transactions on Intelligent Systems and Technology ( Early Access ), DOI: https://dl.acm.org/doi/10.1145/3767722, 2025.
PDF CITEPeng Zhou, Haoran Yu, Yuanting Yan, Xindong Wu. Density-Based Online Concept Evolution Detection in Streaming Features, IEEE Transactions on Emerging Topics in Computational Intelligence ( Early Access ), DOI: 10.1109/TETCI.2025.3597301, 2025.
PDF CITEPeng Zhou, QianChen Chen, Lei Sang, Shu Zhao, Xindong Wu. Unsupervised heterogeneous group streaming feature selection, Pattern Recognition, 171(B):112280, 2025.
PDF CITEPeng Zhou, Qi Wang, Yunyun Zhang, Zhaolong Ling, Xindong Wu. Online Stable Streaming Feature Selection via Feature Aggregation, ACM Transactions on Knowledge Discovery from Data, 19(3):65, 2025.
PDF CITEPeng Zhou, Qihui Tong, Shiji Chen, Yunyun Zhang, Xindong Wu. EACE: Explain Anomaly via Counterfactual Explanations, Pattern Recognition, 164:111532, 2025.
PDF CODE CITEPUBLICATIONS
2024
Peng Zhou, Yufeng Guo, Haoran Yu, Yanting Yan, Yanping Zhang, Xindong Wu. Concept Evolution Detecting over Feature Streams. ACM Transactions on Knowledge Discovery from Data, 18(8):209, 2024.
PDF CODE CITEPeng Zhou, Ji Liang, Yuanting Yan, Shu Zhao, Xindong Wu. Explainable feature selection and ensemble classification via feature polarity. Information Sciences, 676:120818, 2024.
PDF CODE CITEPeng Zhou, Yunyun Zhang, Zhaolong Ling, Yuanting Yan, Shu Zhao, Xindong Wu. Online Heterogeneous Streaming Feature Selection Without Feature Type Information. IEEE Transactions on Big Data, 10(4):470-485, 2024.
PDF CODE CITEPeng Zhou, Xin Jiang, Shu Zhao. Unsupervised technical phrase extraction by incorporating structure and position information. Expert Systems with Applications, 245:123140, 2024.
PDF CITEPeng Zhou, Shuai Zhang, Lin Mu, Yuanting Yan. Online learning from capricious data streams via shared and new feature spaces. Applied Intelligence, 54:9429-9445, 2024.
PDF CODE CITE2023
Peng Zhou, Ni Wang, Shu Zhao, Yanping Zhang, Xindong Wu. Difficult Novel Class Detection in Semisupervised Streaming Data. IEEE Transactions on Neural Networks and Learning Systems, 34(10):6872-6886, 2023.
PDF CODE CITEPeng Zhou, Ni Wang, Shu Zhao, Yanping Zhang. Robust semi-supervised clustering via data transductive warping. Applied Intelligence, 53:1254-1270, 2023.
PDF CODE CITE2022
Peng Zhou, Shu Zhao, Yuanting Yan, Xindong Wu. Online scalable streaming feature selection via dynamic decision. ACM Transactions on Knowledge Discovery from Data, 16(5):87, 2022.
PDF CODE CITEPeng Zhou, Yunyun Zhang, Peipei Li, Xindong Wu. General assembly framework for online streaming feature selection via Rough Set models. Expert Systems With Applications, 204:117520, 2022.
PDF CODE CITEPeng Zhou, Yunyun Zhang, Yuanting Yan, Shu Zhao. Unknown Type Streaming Feature Selection via Maximal Information Coefficient. 2022 IEEE International Conference on Data Mining Workshops (ICDMW), PP:650-657, 2022.
PDF CITE2021
Peng Zhou, Peipei Li, Shu Zhao, Xindong Wu. Feature Interaction for Streaming Feature Selection. IEEE Transactions on Neural Networks and Learning Systems, 32(10):4691-4702, 2021.
PDF CODE CITEPeng Zhou, Ni Wang, Shu Zhao. Online group streaming feature selection considering feature interaction. Knowledge-Based Systems, 226:107157, 2021.
PDF CODE CITEPeng Zhou, Peipei Li, Shu Zhao, Yanping Zhang. Online early terminated streaming feature selection based on Rough Set theory, Applied Soft Computing, 113(B):107993, 2021.
PDF CODE CITE2019
Peng Zhou, Xuegang Hu, Peipei Li, Xindong Wu. Online Streaming Feature Selection Using Adapted Neighborhood Rough Set. Information Sciences, 2019, 481: 258-279.
PDF CODE CITEPeng Zhou, Xuegang Hu, Peipei Li, Xindong Wu. OFS-Density: A novel online streaming feature selection method, Pattern Recognition, 2019, 86:48-61.
PDF CODE CITE2018
Xuegang Hu, Peng Zhou, Peipei Li, Jing Wang, Xindong Wu. A survey on online feature selection with streaming features. Frontiers of Computer Science, 2018, 12 (3): 479-493.
PDF CITE2017
Peng Zhou, Xuegang Hu, Peipei Li, Xindong Wu. Online feature selection for high-dimensional class-imbalanced data. Knowledge-Based Systems, 2017, 136:187-199.
PDF CODE CITEPeng Zhou, Xuegang Hu, Peipei Li. A New Online Feature Selection Method Using Neighborhood Rough Set. IEEE International Conference on Big Knowledge, 2017.8.9 -8.10.
PDF CODE CITEGRANTS & PROJECTS
Online anomaly detection for trapezoidal streaming data
- National Natural Science Foundation of China
- No: 62376001
- 2024/1--2027/12
Key technologies of online anomaly detection for heterogeneous streaming data
- Natural Science Foundation of Anhui Provice of China
- No: 2308085MF215
- 2023/9/1--2026/8/31
Mixed streaming feature selection based on fuzzy rough set
- National Natural Science Foundation of China (Youth Program)
- No: 61906056
- 2020/1--2022/12
PROFESSIONAL SERVICES
Journal Reviewer
- IEEE Transactions on Knowledge and Data Engineering
- IEEE Transactions on Neural Networks and Learning Systems
- ACM Transactions on Knowledge Discovery from Data
- IEEE Transactions on Big Data
- IEEE Transactions on Systems, Man and Cybernetics: Systems
- IEEE Transactions on Emerging Topics in Computational Intelligence
- IEEE Transactions on Fuzzy Systems
- WIREs Data Mining and Knowledge Discovery
- Information Sciences
- Knowledge-Based Systems
- Expert Systems with Applications
- Engineering Applications of Artificial Intelligence
RESOURCES
Code and Software
A list of open source code is maintained on Github. More source code will be released as it is ready for publishing.