  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.
