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题 目：RaSE: Random Subspace Ensemble Classification
主讲人：Yang Feng Associate Professor of Biostatistics at New York University
摘 要：We propose a new model-free ensemble classification framework, Random Subspace Ensemble (RaSE), for sparse classification. In the RaSE algorithm, we aggregate many weak learners, where each weak learner is a base classifier trained in a subspace optimally selected from a collection of random subspaces. To conduct subspace selection, we propose a new criterion, ratio information criterion (RIC), based on weighted Kullback-Leibler divergences. The theoretical analysis includes the risk and Monte-Carlo variance of RaSE classifier, establishing the weak consistency of RIC, and providing an upper bound for the misclassification rate of RaSE classifier. An array of simulations under various models and real-data applications demonstrate the effectiveness of the RaSE classifier in terms of low misclassification rate and accurate feature ranking. The RaSE algorithm is implemented in the R package RaSEn on CRAN.
Yang Feng 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 and semiparametric methods, and bioinformatics. He has published over 30 articles in journals including the Annals of Statistics, JASA, JRSSB, JMLR, Journal of Econometrics, IEEE-PAMI, and Science Advances. He is currently an associate editor for the Journal of Business & Economic Statistics, Statistica Sinica, and Statistical Analysis and Data Mining: The ASA Data Science Journal. His research is partially supported by NSF CAREER Grant DMS-2013789.