Ensembles of Random Sphere Cover Classifiers
This work addresses classification accuracy for machine learning practitioners, but it is incremental as it builds on an existing RSC classifier with tailored ensemble schemes.
The authors tackled the problem of improving classification performance by proposing two ensemble methods for the Randomised Sphere Cover (RSC) classifier, showing that RSC ensembles perform significantly better than some tree-based ensembles and not worse than others on UCI datasets, and that one method outperforms other subspace ensembles on high-dimensional gene expression data.
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; $αβ$RSE, an ensemble based on instance resampling and $α$RSSE, a subspace ensemble. We compare $αβ$RSE and $α$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 $α$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.