Ensembles of k-nearest neighbors and dimensionality reduction

In this paper, ensembles of k-nearest neighbors classifiers are explored for gene expression cancer classification, where each classifier is linked to a randomly selected subset of genes. It is experimentally demonstrated using five datasets that such ensembles can yield both good accuracy and dimensionality reduction. If a characteristic called dataset complexity guides which random subset to include into an ensemble, then the ensemble achieves even better performance.