# Ensembles of Random Sphere Cover Classifiers

arXiv: https://arxiv.org/abs/1409.4936 · HTML: https://mlsanity.com/paper/1409.4936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2014.

## Built on by (most cited first, 1 of 1)

- [A unifying approach on bias and variance analysis for classification](https://mlsanity.com/paper/2101.01765/) (2021)

## Abstract (arXiv)

We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in ensembles. We propose two ensemble methods tailored to the RSC classifier; α βRSE, an ensemble based on instance resampling and αRSSE, a subspace ensemble. We compare α βRSE and αRSSE to tree based ensembles on a set of UCI datasets and demonstrates that RSC ensembles perform significantly better than some of these ensembles, and not significantly worse than the others. We demonstrate via a case study on six gene expression data sets that αRSSE can outperform other subspace ensemble methods on high dimensional data when used in conjunction with an attribute filter. Finally, we perform a set of Bias/Variance decomposition experiments to analyse the source of improvement in comparison to a base classifier.