Junnan Li
Orcid: 0000-0003-1217-5230Affiliations:
- Chongqing Technology and Business University, College of Artificial Intelligence, China
- Chongqing Aerospace Polytechnic, College of Intelligent Information Engineering, China
- Chongqing University of Posts and Telecommunications, School of Computer Science and Technology, China
- Chongqing University, Department of Computer Science, China (PhD)
According to our database1,
Junnan Li
authored at least 17 papers
between 2019 and 2023.
Collaborative distances:
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Bibliography
2023
OALDPC: oversampling approach based on local density peaks clustering for imbalanced classification.
Appl. Intell., December, 2023
A framework based on local cores and synthetic examples generation for self-labeled semi-supervised classification.
Pattern Recognit., 2023
2022
NaNG-ST: A natural neighborhood graph-based self-training method for semi-supervised classification.
Neurocomputing, 2022
A novel hierarchical clustering algorithm with merging strategy based on shared subordinates.
Appl. Intell., 2022
Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution.
IEEE Access, 2022
A Novel Clustering Algorithm with Dynamic Boundary Extraction Strategy Based on Local Gravitation.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022
2021
SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution.
Knowl. Based Syst., 2021
J. Ambient Intell. Humaniz. Comput., 2021
A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors.
Inf. Sci., 2021
Proceedings of the IEEE 45th Annual Computers, Software, and Applications Conference, 2021
2020
An effective framework based on local cores for self-labeled semi-supervised classification.
Knowl. Based Syst., 2020
A parameter-free hybrid instance selection algorithm based on local sets with natural neighbors.
Appl. Intell., 2020
A boosting Self-Training Framework based on Instance Generation with Natural Neighbors for K Nearest Neighbor.
Appl. Intell., 2020
ELS: A Fast Parameter-Free Edition Algorithm With Natural Neighbors-Based Local Sets for k Nearest Neighbor.
IEEE Access, 2020
2019
A self-training method based on density peaks and an extended parameter-free local noise filter for <i>k</i> nearest neighbor.
Knowl. Based Syst., 2019
IEEE Access, 2019