Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice
This work provides an incremental improvement for regression tasks by adapting an existing classification method to avoid kernel tuning.
The authors tackled the problem of reducing generalization error in regression by proposing an ensemble support vector regression model called Regression Random Machines, which achieved lower generalization error without requiring kernel selection in simulations on artificial and real datasets.
Machine learning techniques always aim to reduce the generalized prediction error. In order to reduce it, ensemble methods present a good approach combining several models that results in a greater forecasting capacity. The Random Machines already have been demonstrated as strong technique, i.e: high predictive power, to classification tasks, in this article we propose an procedure to use the bagged-weighted support vector model to regression problems. Simulation studies were realized over artificial datasets, and over real data benchmarks. The results exhibited a good performance of Regression Random Machines through lower generalization error without needing to choose the best kernel function during tuning process.