FEMaR: A Finite Element Machine for Regression Problems

Abstract

Regression-based tasks have been the forerunner regarding the application of machine learning tools in the context of data mining. Problems related to price and stock prediction, selling estimation, and weather forecasting are commonly used as benchmarking for the comparison of regression techniques, just to name a few. Neural Networks, Decision Trees and Support Vector Machines are the most widely used approaches concerning regression-oriented applications, since they can generalize well in a number of different applications. In this work, we propose an efficient and effective regression technique based on the Finite Element Method (FEM) theory, hereinafter called Finite Element Machine for Regression (FEMaR). The proposed approach has only one parameter and it has a quadratic complexity for both training and classification phases when we use basis functions that obey some properties, as well as we show the proposed approach can obtain very competitive results when compared against some state-of-the-art regression techniques.