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A survey on multi‐output regression
Author(s) -
Borchani Hanen,
Varando Gherardo,
Bielza Concha,
Larrañaga Pedro
Publication year - 2015
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1157
Subject(s) - computer science , regression , adaptation (eye) , task (project management) , regression analysis , machine learning , transformation (genetics) , regression testing , data mining , software , artificial intelligence , statistics , software development , engineering , mathematics , software construction , biochemistry , physics , chemistry , systems engineering , optics , gene , programming language
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