Optimizing model-agnostic Random Subspace ensembles

This paper presents a model-agnostic ensemble approach for supervised learning. The proposed approach alternates between (1) learning an ensemble of models using a parametric version of the Random Subspace approach, in which feature subsets are sampled according to Bernoulli distributions, and (2) identifying the parameters of the Bernoulli distributions that minimize the generalization error of the ensemble model. Parameter optimization is rendered tractable by using an importance sampling approach able to estimate the expected model output for any given parameter set, without the need to learn new models. While the degree of randomization is controlled by a hyper-parameter in standard Random Subspace, it has the advantage to be automatically tuned in our parametric version. Furthermore, model-agnostic feature importance scores can be easily derived from the trained ensemble model. We show the good performance of the proposed approach, both in terms of prediction and feature ranking, on simulated and real-world datasets. We also show that our approach can be successfully used for the reconstruction of gene regulatory networks.