We propose a flexible ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on KullbackLeibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the crossvalidation error, minimizing leaveoneout error. And the choices of base classifiers are also various, for instance, linear discriminant analysis, quadratic discriminant analysis, knearest neighbor, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. In addition, to relax the requirement of the number of random subspaces to be generated, we propose an iterative version of RaSE, which is shown to be effective under many sparse binary classification settings.
Package details 


Author  Ye Tian [aut, cre] and Yang Feng [aut] 
Maintainer  Ye Tian <ye.t@columbia.edu> 
License  GPL2 
Version  1.1.0 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.