Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer


Yang Feng

Yang Feng
Yang Feng
New York University

RaSE: Random Subspace Ensemble Classification

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. This is joint work with Ye Tian.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar givenĀ has a direct impact upon ourĀ students and faculty.