RT info:eu-repo/semantics/article
T1 Rotation Forest for multi-target regression
A1 Rodríguez Diez, Juan José
A1 Juez Gil, Mario
A1 López Nozal, Carlos
A1 Arnaiz González, Álvar
K1 Multi-target regression
K1 Ensemble
K1 Rotation Forest
K1 Informática
K1 Computer science
AB The prediction of multiple numeric outputs at the same time is called multi-target regression (MTR), and it has gainedattention during the last decades. This task is a challenging research topic in supervised learning because it poses additionaldifficulties to traditional single-target regression (STR), and many real-world problems involve the prediction of multipletargets at once. One of the most successful approaches to deal with MTR, although not the only one, consists in transformingthe problem in several STR problems, whose outputs will be combined building up the MTR output. In this paper, theRotation Forest ensemble method, previously proposed for single-label classification and single-target regression, is adaptedto MTR tasks and tested with several regressors and data sets. Our proposal rotates the input space in an efficient and novelfashion, avoiding extra rotations forced by MTR problem decomposition. Four approaches for MTR are used: single-target(ST), stacked-single target (SST), Ensembles of Regressor Chains (ERC), and Multi-target Regression via Quantization(MRQ). For assessing the benefits of the proposal, a thorough experimentation with 28 MTR data sets and statistical testsare used, concluding that Rotation Forest, adapted by means of these approaches, outperforms other popular ensembles,such as Bagging and Random Forest.
PB Springer
SN 1868-8071
YR 2022
FD 2022-02
LK http://hdl.handle.net/10259/6645
UL http://hdl.handle.net/10259/6645
LA eng
NO Ministerio de Economía y Competitividad of the Spanish Government under project TIN2015-67534-P (MINECO-FEDER, UE), by the Junta de Castilla y León under project BU085P17 (JCyL/FEDER, UE) (both projects co-financed through European Union FEDER funds), and by the Consejería de Educación of the Junta de Castilla y León and the European Social Fund with the EDU/1100/2017 pre-doctoral grant.
DS Repositorio Institucional de la Universidad de Burgos
RD 22-nov-2024