Classification of DNA microarray data with Random Projection Ensembles of Polynomial SVMs

In this paper we propose and experimentally analyze ensemble methods based on random projections (as feature extraction method) and SVM with polynomial kernels (as learning algorithm). We show that, under suitable conditions, polynomial kernels are approximately preserved by random projections, with a degradation related to the square of the degree of the polynomial. Experimental results with Random Subspace and Random Projection ensembles of polynomial SVMs, support the hypothesis the low degree polynomial kernels, introducing with high probability lower distortions in the projected data, are better suited to the classification of high dimensional DNA microarray data.