Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice

• URL: http://arxiv.org/abs/2003.12643v1
• Date: Fri, 27 Mar 2020 21:30:59 GMT
• Title: Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice
• Authors: Anderson Ara, Mateus Maia, Samuel Mac\^edo and Francisco Louzada
• Abstract summary: In this article we propose a procedure to use the bagged-weighted support vector model to regression problems. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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