Statistics and Data Science Seminar
Yang Feng
New York University
RaSE: Random Subspace Ensemble Classification
Abstract: 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.
Wednesday November 11, 2020 at 4:00 PM in Zoom