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We propose and evaluate alternative ensemble schemes for a new instance basedlearning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fusesinstances into spheres, then bases classification on distance to spheres ratherthan distance to instances. The randomised nature of RSC makes it ideal for usein ensembles. We propose two ensemble methods tailored to the RSC classifier;$\alpha \beta$RSE, an ensemble based on instance resampling and $\alpha$RSSE, asubspace ensemble. We compare $\alpha \beta$RSE and $\alpha$RSSE to tree basedensembles on a set of UCI datasets and demonstrates that RSC ensembles performsignificantly better than some of these ensembles, and not significantly worsethan the others. We demonstrate via a case study on six gene expression datasets that $\alpha$RSSE can outperform other subspace ensemble methods on highdimensional data when used in conjunction with an attribute filter. Finally, weperform a set of Bias/Variance decomposition experiments to analyse the sourceof improvement in comparison to a base classifier.

Ensembles of Random Sphere Cover Classifiers