| Deep forest ZH Zhou, J Feng National Science Review 6 (1), 74-86, 2019 | 1554 | 2019 |
| Deep MIML Network J Feng, ZH Zhou AAAI 2017, 2017 | 166 | 2017 |
| Multi-Layered Gradient Boosting Decision Trees J Feng, Y Yu, ZH Zhou NIPS 18, 2018 | 136 | 2018 |
| Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder J Feng, QZ Cai, ZH Zhou NeurIPS 2019, 2019 | 93 | 2019 |
| Distributed deep forest and its application to automatic detection of cash-out fraud YL Zhang, J Zhou, W Zheng, J Feng, L Li, Z Liu, M Li, Z Zhang, C Chen, ... ACM Transactions on Intelligent Systems and Technology, 2019 | 83 | 2019 |
| AutoEncoder by Forest J Feng, ZH Zhou AAAI 2018, 2018 | 73 | 2018 |
| Reconstruction-based anomaly detection with completely random forest YX Xu, M Pang, J Feng, KM Ting, Y Jiang, ZH Zhou Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 13 | 2021 |
| Soft gradient boosting machine J Feng, YX Xu, Y Jiang, ZH Zhou arXiv preprint arXiv:2006.04059, 2020 | 13 | 2020 |
| Federated soft gradient boosting machine for streaming data J Feng, YX Xu, YG Wang, Y Jiang Federated Learning: Privacy and Incentive, 93-107, 2020 | 3 | 2020 |
| Deep Forest: Towards an Alternative to Deep Neural Networks ZH Zhou, J Feng IJCAI 17, 2017 | | 2017 |
Zhi-Hua ZhouNanjing UniversityE-mail megerősítve itt: nju.edu.cn
