Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression

Abstract

Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods, the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM), in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential, emphasizing the utility of the problem transformation especially with the EMLM.