A survey on multi‐output regression

TLDR

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This study surveys state-of-the-art multi-output regression methods, categorizing them into problem transformation and algorithm adaptation approaches. The survey also reviews common evaluation metrics, publicly available datasets, and open-source software frameworks for multi-output regression. The article is published in WIREs Data Mining Knowledge Discovery 2015 (5:216–233) with doi 10.1002/widm.1157 and is categorized under Technologies > Machine Learning.

Abstract

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi‐output regression. This study provides a survey on state‐of‐the‐art multi‐output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi‐output regression real‐world problems, as well as open‐source software frameworks. WIREs Data Mining Knowl Discov 2015, 5:216–233. doi: 10.1002/widm.1157 This article is categorized under: Technologies > Machine Learning