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ORCIDJunnan Li 0004
Person information
- affiliation: Chongqing Technology and Business University, College of Artificial Intelligence, China
- affiliation: Chongqing Aerospace Polytechnic, College of Intelligent Information Engineering, China
- affiliation: Chongqing University of Posts and Telecommunications, School of Computer Science and Technology, China
- affiliation (PhD): Chongqing University, Department of Computer Science, China
Other persons with the same name
- Junnan Li 0001
![[0000-0002-1405-2034]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-1405-2034")
— National University of Singapore, Graduate School for Integrative Sciences and Engineering, Singapore (and 1 more) - Junnan Li 0002 — National University of Defense Technology, College of Computer Science and Technology, Changsha, China
- Junnan Li 0003 — Fudan University, School of Computer Science, Shanghai, China
- Junnan Li 0005 — Xi'an Jiao Tong University, School of Human Settlements and Civil Engineering, China
- Junnan Li 0006 — Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China
- Junnan Li 0007 — State Grid Henan Electric Power Company Marketing Service Center, Zhengzhou, China
- Junnan Li 0008 — TU Munich, Munich Institute of Robotics and Machine Intelligence, Germany
- Junnan Li 0009 — University of Electronic Science and Technology of China, School of Cybersecurity, Chengdu, China (and 1 more)
- Junnan Li 0010 — Yunnan Power Grid Co., Ltd, Kunming, China
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2020 – today
- 2023
[j15]Junnan Li, Qingsheng Zhu:
OALDPC: oversampling approach based on local density peaks clustering for imbalanced classification. Appl. Intell. 53(24): 30987-31017 (2023)- [j14]Junnan Li, Mingqiang Zhou, Qingsheng Zhu, Quanwang Wu:
A framework based on local cores and synthetic examples generation for self-labeled semi-supervised classification. Pattern Recognit. 134: 109060 (2023) - 2022
- [j13]Zhiju Zhang, Junnan Li:
Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution. IEEE Access 10: 74045-74058 (2022) - [j12]Jinxin Shi, Qingsheng Zhu, Junnan Li:
A novel hierarchical clustering algorithm with merging strategy based on shared subordinates. Appl. Intell. 52(8): 8635-8650 (2022) - [j11]Junnan Li:
NaNG-ST: A natural neighborhood graph-based self-training method for semi-supervised classification. Neurocomputing 514: 268-284 (2022) - [c2]Jiangmei Luo, Qingsheng Zhu, Junnan Li, Dongdong Cheng, Mingqiang Zhou:
A Novel Clustering Algorithm with Dynamic Boundary Extraction Strategy Based on Local Gravitation. PAKDD (2) 2022: 174-186 - 2021
- [j10]Junnan Li, Qingsheng Zhu, Quanwang Wu, Fan Zhu:
A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors. Inf. Sci. 565: 438-455 (2021) - [j9]Suwen Zhao, Junnan Li:
A semi-supervised self-training method based on density peaks and natural neighbors. J. Ambient Intell. Humaniz. Comput. 12(2): 2939-2953 (2021) - [j8]Junnan Li, Qingsheng Zhu, Quanwang Wu, Zhiyong Zhang, Yanlu Gong, Ziqing He, Fan Zhu:
SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution. Knowl. Based Syst. 223: 107056 (2021) - [j7]Zhiyong Zhang, Qingsheng Zhu, Fan Zhu, Junnan Li, Dongdong Cheng, Yi Liu, Jiangmei Luo:
Density decay graph-based density peak clustering. Knowl. Based Syst. 224: 107075 (2021) - [c1]Jinxin Shi, Qingsheng Zhu, Junnan Li, Ji Liu, Dongdong Cheng:
Hierarchical Clustering Based on Local Cores and Sharing Concept. COMPSAC 2021: 284-289 - 2020
- [j6]Suwen Zhao, Junnan Li:
ELS: A Fast Parameter-Free Edition Algorithm With Natural Neighbors-Based Local Sets for k Nearest Neighbor. IEEE Access 8: 123773-123782 (2020) - [j5]Junnan Li, Qingsheng Zhu, Quanwang Wu:
A parameter-free hybrid instance selection algorithm based on local sets with natural neighbors. Appl. Intell. 50(5): 1527-1541 (2020) - [j4]Junnan Li, Qingsheng Zhu:
A boosting Self-Training Framework based on Instance Generation with Natural Neighbors for K Nearest Neighbor. Appl. Intell. 50(11): 3535-3553 (2020) - [j3]Junnan Li, Qingsheng Zhu, Quanwang Wu, Dongdong Cheng:
An effective framework based on local cores for self-labeled semi-supervised classification. Knowl. Based Syst. 197: 105804 (2020)
2010 – 2019
- 2019
- [j2]Junnan Li, Qingsheng Zhu:
Semi-Supervised Self-Training Method Based on an Optimum-Path Forest. IEEE Access 7: 36388-36399 (2019) - [j1]Junnan Li, Qingsheng Zhu, Quanwang Wu:
A self-training method based on density peaks and an extended parameter-free local noise filter for k nearest neighbor. Knowl. Based Syst. 184 (2019)
Coauthor Index
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