# RaSE: Random Subspace Ensemble Classification

arXiv: https://arxiv.org/abs/2006.08855 · HTML: https://mlsanity.com/paper/2006.08855/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Optimizing model-agnostic Random Subspace ensembles](https://mlsanity.com/paper/2109.03099/) (2021)
- [RaSE: A Variable Screening Framework via Random Subspace Ensembles](https://mlsanity.com/paper/2102.03892/) (2021)
- [Statistical optimality conditions for compressive ensembles](https://mlsanity.com/paper/2106.01092/) (2021)
- [Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance](https://mlsanity.com/paper/2403.00975/) (2024)
- [Machine Collaboration](https://mlsanity.com/paper/2105.02569/) (2021)
- [Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learning](https://mlsanity.com/paper/2112.02589/) (2021)
- [Conjunction Subspaces Test for Conformal and Selective Classification](https://mlsanity.com/paper/2410.12297/) (2024)

## Abstract (arXiv)

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. In addition, we show that in a high-dimensional framework, the number of random subspaces needs to be very large to guarantee that a subspace covering signals is selected. Therefore, we propose an iterative version of the RaSE algorithm and prove that under some specific conditions, a smaller number of generated random subspaces are needed to find a desirable subspace through iteration. 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.