RaSE: Random Subspace Ensemble Classification

发布时间：2021-06-21 作者：浏览次数：

Speaker:	冯阳	DateTime:	2021年6月30日（周三）上午10:30-11:30
Brief Introduction to Speaker: 冯阳，哥伦比亚大学。		Place:	腾讯会议（会议号请联系晏挺老师获取）
Abstract:We propose a flexible 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 divergence. The theoretical analysis includes the risk and Monte-Carlo variance of the RaSE classifier,establishing the screening consistency and weak consistency of RIC,and providing an upper bound for the misclassification rate of the RaSE classifier. An array of simulations under various models and real-data applications demonstrate the effectiveness and robustness of the RaSE classifier and its iterative version in terms of low misclassification rate and accurate feature ranking. The RaSE algorithm is implemented in the R package RaSEn on CRAN.

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