| Learning k for kNN Classification S Zhang, X Li, M Zong, X Zhu, D Cheng ACM Transactions on Intelligent Systems and Technology (TIST) 8 (3), 1-19, 2017 | 709 | 2017 |
| Efficient kNN classification algorithm for big data Z Deng, X Zhu, D Cheng, M Zong, S Zhang Neurocomputing 195, 143-148, 2016 | 620 | 2016 |
| A novel kNN algorithm with data-driven k parameter computation S Zhang, D Cheng, Z Deng, M Zong, X Deng Pattern Recognition Letters 109, 44-54, 2018 | 216 | 2018 |
| Graph self-representation method for unsupervised feature selection R Hu, X Zhu, D Cheng, W He, Y Yan, J Song, S Zhang Neurocomputing 220, 130-137, 2017 | 164 | 2017 |
| kNN Algorithm with Data-Driven k Value D Cheng, S Zhang, Z Deng, Y Zhu, M Zong Advanced Data Mining and Applications: 10th International Conference, ADMA …, 2014 | 111 | 2014 |
| Efficient kNN algorithm based on graph sparse reconstruction S Zhang, M Zong, K Sun, Y Liu, D Cheng Advanced Data Mining and Applications: 10th International Conference, ADMA …, 2014 | 47 | 2014 |
| Feature selection by combining subspace learning with sparse representation D Cheng, S Zhang, X Liu, K Sun, M Zong Multimedia Systems 23, 285-291, 2017 | 28 | 2017 |
| Self-representation nearest neighbor search for classification S Zhang, D Cheng, M Zong, L Gao Neurocomputing 195, 137-142, 2016 | 27 | 2016 |
| Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables D Cheng, J Li, L Liu, K Yu, T Duy Lee, J Liu IEEE Transactions on Neural Networks and Learning Systems, 1-13, 2022 | 22 | 2022 |
| Supervised feature selection algorithm via discriminative ridge regression S Zhang, D Cheng, R Hu, Z Deng World Wide Web 21, 1545-1562, 2018 | 22 | 2018 |
| Low-rank feature selection for multi-view regression R Hu, D Cheng, W He, G Wen, Y Zhu, J Zhang, S Zhang Multimedia Tools and Applications 76, 17479-17495, 2017 | 18 | 2017 |
| Unsupervised feature selection for visual classification via feature-representation property W He, X Zhu, D Cheng, R Hu, S Zhang Neurocomputing 236, 5-13, 2017 | 18 | 2017 |
| Sufficient Dimension Reduction for Average Causal Effect Estimation D Cheng, J Li, L Liu, TD Le, J Liu, K Yu Data Mining and Knowledge Discovery, https://doi.org/10.1007/s10618-022-00832, 2022 | 16 | 2022 |
| Spectral clustering based on hypergraph and self-re-presentation Y Li, S Zhang, D Cheng, W He, G Wen, Q Xie Multimedia Tools and Applications 76, 17559-17576, 2017 | 16 | 2017 |
| Improved spectral clustering algorithm based on similarity measure J Yan, D Cheng, M Zong, Z Deng Advanced Data Mining and Applications: 10th International Conference, ADMA …, 2014 | 15 | 2014 |
| Data-driven causal effect estimation based on graphical causal modelling: A survey D Cheng, J Li, L Liu, J Liu, TD Le ACM Computing Surveys, https://doi.org/10.1145/3636423, 1-35, 2023 | 13 | 2023 |
| Graph feature selection for dementia diagnosis Y Zhu, Z Zhong, W Cao, D Cheng Neurocomputing 195, 19-22, 2016 | 13 | 2016 |
| Causal query in observational data with hidden variables D Cheng, J Li, L Liu, J Liu, K Yu, TD Le ECAI2020, 2020 | 11 | 2020 |
| Application of ToF-SIMS to predict contact angles of pyrite particles S Xu, D Cheng, W Skinner, SB e Abreu Minerals Engineering 147, 106168, 2020 | 10 | 2020 |
| Sparse sample self-representation for subspace clustering Z Deng, S Zhang, L Yang, M Zong, D Cheng Neural Computing and Applications 29, 43-49, 2018 | 10 | 2018 |
Shichao ZhangGuangxi Normal UniversityEmail verificata su mailbox.gxnu.edu.cn
