| Online feature selection with streaming features X Wu, K Yu, W Ding, H Wang, X Zhu IEEE transactions on pattern analysis and machine intelligence 35 (5), 1178-1192, 2012 | 333 | 2012 |
| Online streaming feature selection X Wu, K Yu, H Wang, W Ding Proceedings of the 27th international conference on machine learning (ICML …, 2010 | 182 | 2010 |
| Causality-based feature selection: Methods and evaluations K Yu, X Guo, L Liu, J Li, H Wang, Z Ling, X Wu ACM Computing Surveys (CSUR) 53 (5), 1-36, 2020 | 169 | 2020 |
| Scalable and accurate online feature selection for big data K Yu, X Wu, W Ding, J Pei ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (2), 1-39, 2016 | 148 | 2016 |
| Towards scalable and accurate online feature selection for big data K Yu, X Wu, W Ding, J Pei 2014 IEEE International Conference on Data Mining, 660-669, 2014 | 135 | 2014 |
| Multi-source causal feature selection K Yu, L Liu, J Li, W Ding, TD Le IEEE transactions on pattern analysis and machine intelligence 42 (9), 2240-2256, 2019 | 107 | 2019 |
| A unified view of causal and non-causal feature selection K Yu, L Liu, J Li ACM Transactions on Knowledge Discovery from Data (TKDD) 15 (4), 1-46, 2021 | 83 | 2021 |
| Learning common and label-specific features for multi-label classification with correlation information J Li, P Li, X Hu, K Yu Pattern recognition 121, 108259, 2022 | 74 | 2022 |
| Accurate Markov boundary discovery for causal feature selection X Wu, B Jiang, K Yu, H Chen IEEE transactions on cybernetics 50 (12), 4983-4996, 2019 | 72 | 2019 |
| miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase T Xu, N Su, L Liu, J Zhang, H Wang, W Zhang, J Gui, K Yu, J Li, TD Le BMC bioinformatics 19, 179-188, 2018 | 66 | 2018 |
| LOFS: A library of online streaming feature selection K Yu, W Ding, X Wu Knowledge-Based Systems 113, 1-3, 2016 | 59 | 2016 |
| Towards efficient and effective discovery of Markov blankets for feature selection H Wang, Z Ling, K Yu, X Wu Information Sciences 509, 227-242, 2020 | 57 | 2020 |
| BAMB: A balanced Markov blanket discovery approach to feature selection Z Ling, K Yu, H Wang, L Liu, W Ding, X Wu ACM Transactions on Intelligent Systems and Technology (TIST) 10 (5), 1-25, 2019 | 56 | 2019 |
| Semi-supervised classification on data streams with recurring concept drift and concept evolution X Zheng, P Li, X Hu, K Yu Knowledge-Based Systems 215, 106749, 2021 | 52 | 2021 |
| Multi-label causal feature selection X Wu, B Jiang, K Yu, H Chen, C Miao Proceedings of the AAAI conference on artificial intelligence 34 (04), 6430-6437, 2020 | 49 | 2020 |
| Joint semi-supervised feature selection and classification through Bayesian approach B Jiang, X Wu, K Yu, H Chen Proceedings of the AAAI conference on artificial intelligence 33 (01), 3983-3990, 2019 | 46 | 2019 |
| Learning causal representations for robust domain adaptation S Yang, K Yu, F Cao, L Liu, H Wang, J Li IEEE Transactions on Knowledge and Data Engineering 35 (3), 2750-2764, 2021 | 41 | 2021 |
| Mining Markov blankets without causal sufficiency K Yu, L Liu, J Li, H Chen IEEE transactions on neural networks and learning systems 29 (12), 6333-6347, 2018 | 40 | 2018 |
| Classification with streaming features: An emerging-pattern mining approach K Yu, W Ding, DA Simovici, H Wang, J Pei, X Wu ACM Transactions on Knowledge Discovery from Data (TKDD) 9 (4), 1-31, 2015 | 40 | 2015 |
| Learning markov blankets from multiple interventional data sets K Yu, L Liu, J Li IEEE transactions on neural networks and learning systems 31 (6), 2005-2019, 2019 | 33* | 2019 |
Xindong WuHefei University of Technology在 hfut.edu.cn 的电子邮件经过验证
