Ensembles of Random Sphere Cover Classifiers

TL;DR

This paper introduces ensemble methods for the Randomised Sphere Cover (RSC) classifier, demonstrating improved performance over some tree-based ensembles and effectiveness on high-dimensional gene expression data.

Contribution

The paper proposes two novel ensemble schemes for RSC, a new instance-based classifier, and evaluates their performance against existing ensemble methods on various datasets.

Findings

RSC ensembles outperform some tree-based ensembles.

$ ext{α}$RSSE performs well on high-dimensional gene expression data.

Bias/Variance analysis explains sources of improvement.

Abstract

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; $\alpha \beta$RSE, an ensemble based on instance resampling and $\alpha$RSSE, a subspace ensemble. We compare $\alpha \beta$RSE and $\alpha$RSSE 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 $\alpha$RSSE 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.