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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A self-training method based on density peaks and an extended parameter-free local noise filter for k nearest neighbor
Junnan Li, Qingsheng Zhu, Quanwang Wu
Knowledge-Based Systems (2019) Vol. 184, pp. 104895-104895
Closed Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors
Junnan Li, Qingsheng Zhu, Quanwang Wu, et al.
Information Sciences (2021) Vol. 565, pp. 438-455
Closed Access | Times Cited: 148
SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution
Junnan Li, Qingsheng Zhu, Quanwang Wu, et al.
Knowledge-Based Systems (2021) Vol. 223, pp. 107056-107056
Closed Access | Times Cited: 70
Sampling technique for noisy and borderline examples problem in imbalanced classification
Abhishek Dixit, Ashish Mani
Applied Soft Computing (2023) Vol. 142, pp. 110361-110361
Closed Access | Times Cited: 19
Extended natural neighborhood for SMOTE and its variants in imbalanced classification
Hongjiao Guan, Long Zhao, Xiangjun Dong, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 124, pp. 106570-106570
Closed Access | Times Cited: 16
NanBDOS: Adaptive and parameter-free borderline oversampling via natural neighbor search for class-imbalance learning
Qiangkui Leng, Jiamei Guo, Erjie Jiao, et al.
Knowledge-Based Systems (2023) Vol. 274, pp. 110665-110665
Closed Access | Times Cited: 15
An effective framework based on local cores for self-labeled semi-supervised classification
Junnan Li, Qingsheng Zhu, Quanwang Wu, et al.
Knowledge-Based Systems (2020) Vol. 197, pp. 105804-105804
Closed Access | Times Cited: 33
Fast semi-supervised self-training algorithm based on data editing
Bing Li, Jikui Wang, Zhengguo Yang, et al.
Information Sciences (2023) Vol. 626, pp. 293-314
Closed Access | Times Cited: 12
Oversampling framework based on sample subspace optimization with accelerated binary particle swarm optimization for imbalanced classification
Junnan Li
Applied Soft Computing (2024) Vol. 162, pp. 111708-111708
Closed Access | Times Cited: 4
A self-training method based on fast binary bare-bones particle swarm optimization for semi-supervised classification
Junnan Li
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 108546-108546
Closed Access | Times Cited: 4
An efficient framework based on local multi-representatives and noise-robust synthetic example generation for self-labeled semi-supervised classification
Junnan Li, Shun Fu, Wei Fu, et al.
Neural Networks (2025) Vol. 185, pp. 107142-107142
Closed Access
A parameter-free self-training algorithm based on the three successive confirmation rule
Jikui Wang, Wei Zhao, Qingsheng Shang, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 144, pp. 110165-110165
Closed Access
Fuzzy rough label modification learning for unlabeled and mislabeled data
Changzhong Wang, Changyue Wang, Shuang An, et al.
Fuzzy Sets and Systems (2025), pp. 109315-109315
Closed Access
Self-labeled framework with semi-supervised ball K-means clustering-based synthetic example generation for semi-supervised classification in industrial applications
Junnan Li, Lufeng Wang, Shun Fu, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 150, pp. 110528-110528
Closed Access
A parameter-free hybrid instance selection algorithm based on local sets with natural neighbors
Junnan Li, Qingsheng Zhu, Quanwang Wu
Applied Intelligence (2020) Vol. 50, Iss. 5, pp. 1527-1541
Closed Access | Times Cited: 28
A boosting Self-Training Framework based on Instance Generation with Natural Neighbors for K Nearest Neighbor
Junnan Li, Qingsheng Zhu
Applied Intelligence (2020) Vol. 50, Iss. 11, pp. 3535-3553
Closed Access | Times Cited: 28
Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems
Ming Yan, Yewang Chen, Xiaoliang Hu, et al.
Journal of Systems Architecture (2021) Vol. 118, pp. 102212-102212
Closed Access | Times Cited: 24
Deep collaborative learning with class-rebalancing for semi-supervised change detection in SAR images
Xuan Hou, Yunpeng Bai, Yefan Xie, et al.
Knowledge-Based Systems (2023) Vol. 264, pp. 110281-110281
Closed Access | Times Cited: 10
Classification with noisy labels through tree-based models and semi-supervised learning: A case study of lithology identification
Xinyi Zhu, Hongbing Zhang, Rui Zhu, et al.
Expert Systems with Applications (2023) Vol. 240, pp. 122506-122506
Closed Access | Times Cited: 10
A robust self-training algorithm based on relative node graph
Jikui Wang, Huiyu Duan, Cuihong Zhang, et al.
Applied Intelligence (2024) Vol. 55, Iss. 1
Closed Access | Times Cited: 3
A novel synthetic minority oversampling technique based on relative and absolute densities for imbalanced classification
Ruijuan Liu
Applied Intelligence (2022) Vol. 53, Iss. 1, pp. 786-803
Closed Access | Times Cited: 14
A semi-supervised self-training method based on density peaks and natural neighbors
Suwen Zhao, Junnan Li
Journal of Ambient Intelligence and Humanized Computing (2020) Vol. 12, Iss. 2, pp. 2939-2953
Closed Access | Times Cited: 21
A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM
Jiang Shen, Jiachao Wu, Man Xu, et al.
Computational and Mathematical Methods in Medicine (2021) Vol. 2021, pp. 1-15
Open Access | Times Cited: 20
Maximum a posteriori estimation and filtering algorithm for numerical label noise
Gaoxia Jiang, Zhengying Li, Wenjian Wang
Applied Intelligence (2024) Vol. 54, Iss. 19, pp. 8841-8855
Closed Access | Times Cited: 2
Noise-Tolerant Co-Trained Semisupervised Soft Sensor Model for Industrial Process
Qi Lei, Huiru Wang
IEEE Sensors Journal (2022) Vol. 22, Iss. 20, pp. 19411-19423
Closed Access | Times Cited: 12
An oversampling method based on differential evolution and natural neighbors
Xialin Wang, Yanying Li, Jiaoni Zhang, et al.
Applied Soft Computing (2023) Vol. 149, pp. 110952-110952
Closed Access | Times Cited: 6
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