Semi-Supervised Self-Training Method Based on an Optimum-Path Forest. - Junnan Li 004; Qingsheng Zhu (IEEE Access, 2019)

A self-training method based on density peaks and an extended parameter-free local noise filter for k nearest neighbor. - Junnan Li 004; Qingsheng Zhu; Quanwang Wu (Knowl. Based Syst., 2019)

ELS: A Fast Parameter-Free Edition Algorithm With Natural Neighbors-Based Local Sets for k Nearest Neighbor. - Suwen Zhao; Junnan Li 004 (IEEE Access, 2020)

A boosting Self-Training Framework based on Instance Generation with Natural Neighbors for K Nearest Neighbor. - Junnan Li 004; Qingsheng Zhu (Appl. Intell., 2020)

A parameter-free hybrid instance selection algorithm based on local sets with natural neighbors. - Junnan Li 004; Qingsheng Zhu; Quanwang Wu (Appl. Intell., 2020)

An effective framework based on local cores for self-labeled semi-supervised classification. - Junnan Li 004; Qingsheng Zhu; Quanwang Wu; Dongdong Cheng (Knowl. Based Syst., 2020)

Hierarchical Clustering Based on Local Cores and Sharing Concept. - Jinxin Shi; Qingsheng Zhu; Junnan Li 004; Ji Liu 006; Dongdong Cheng (COMPSAC, 2021)

A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors. - Junnan Li 004; Qingsheng Zhu; Quanwang Wu; Fan Zhu (Inf. Sci., 2021)

A semi-supervised self-training method based on density peaks and natural neighbors. - Suwen Zhao; Junnan Li 004 (J. Ambient Intell. Humaniz. Comput., 2021)

SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution. - Junnan Li 004; Qingsheng Zhu; Quanwang Wu; Zhiyong Zhang; Yanlu Gong; Ziqing He; Fan Zhu (Knowl. Based Syst., 2021)

Density decay graph-based density peak clustering. - Zhiyong Zhang; Qingsheng Zhu; Fan Zhu; Junnan Li 004; Dongdong Cheng; Yi Liu; Jiangmei Luo (Knowl. Based Syst., 2021)

A Novel Clustering Algorithm with Dynamic Boundary Extraction Strategy Based on Local Gravitation. - Jiangmei Luo; Qingsheng Zhu; Junnan Li 004; Dongdong Cheng; Mingqiang Zhou (PAKDD (2), 2022)

Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution. - Zhiju Zhang; Junnan Li 004 (IEEE Access, 2022)

A novel hierarchical clustering algorithm with merging strategy based on shared subordinates. - Jinxin Shi; Qingsheng Zhu; Junnan Li 004 (Appl. Intell., 2022)

NaNG-ST: A natural neighborhood graph-based self-training method for semi-supervised classification. - Junnan Li 004 (Neurocomputing, 2022)

A framework based on local cores and synthetic examples generation for self-labeled semi-supervised classification. - Junnan Li 004; Mingqiang Zhou; Qingsheng Zhu; Quanwang Wu (Pattern Recognit., 2023)

OALDPC: oversampling approach based on local density peaks clustering for imbalanced classification. - Junnan Li 004; Qingsheng Zhu (Appl. Intell., 2023)

BPSO-SLM: a binary particle swarm optimization-based self-labeled method for semi-supervised classification. - Ruijuan Liu; Junnan Li 004 (Int. J. Mach. Learn. Cybern., 2024)

Correction to: A heuristic hybrid instance reduction approach based on adaptive relative distance and k-means clustering. - Junnan Li 004; Qing Zhao; Shuang Liu (J. Supercomput., 2024)