报告人：Yang Feng（New York University）

时间：2021年11月12日10:00开始

腾讯会议ID：839 614 557 

会议链接：https://meeting.tencent.com/dm/yjUyAKLJtfgi 

摘要：We propose a flexible ensemble framework, Random Subspace Ensemble (RaSE). In the RaSE algorithm, we aggregate many weak learners, where each weak learner is trained in a subspace optimally selected from a collection of random subspaces using a base method. In addition, we show that in a high-dimensional framework, the number of random subspaces needs to be very large to guarantee that a subspace covering signals is selected. Therefore, we propose an iterative version of the RaSE algorithm and prove that under some specific conditions, a smaller number of generated random subspaces are needed to find a desirable subspace through iteration. We study the RaSE framework for classification where a general upper bound for the misclassification rate was derived, and for screening where the sure screening property was established. An extension called Super RaSE was proposed to allow the algorithm to select the optimal pair of base method and subspace during the ensemble process. The RaSE framework is implemented in the R package RaSEn on CRAN.

Relevant Papers and the R Package:

[1] Tian, Y., & Feng, Y. (2021). RaSE: Random Subspace Ensemble Classification. J. Mach. Learn. Res., 22, 45-1.

[2] Tian, Y., & Feng, Y. (2021). RaSE: A variable screening framework via random subspace ensembles. Journal of the American Statistical Association, (just-accepted), 1-30.

[3] Zhu, J., & Feng, Y. (2021). Super RaSE: Super Random Subspace Ensemble Classification. Manuscript.

[4] R Package RaSEn: https://cran.r-project.org/web/packages/RaSEn/index.html

简介:Yang Feng(冯阳) is an associate professor of biostatistics in the School of Global Public Health at New York University. Feng focuses on developing and applying machine learning methods in public health, high-dimensional data analysis, network models, nonparametric  methods, and bioinformatics. He has published over 50 papers in journals including the Annals of Statistics, JASA, JRSSB, Biometrika,  IEEE-PAMI, JMLR, Science Advances, JoE, JBES, etc. He is currently an associate editor for journals including JASA, JBES, and Statistica Sinica. His research is supported by NSF and NIH.

邀请人：夏小超

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