2026-04-28T08:15:22Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/98252025-05-12T06:34:04Zcom_10259_5645com_10259_5086com_10259_2604col_10259_9820col_10259_5684
Pacheco Bonrostro, Joaquín
410
600
0000-0002-7462-8680
Casado Yusta, Silvia
666
600
0000-0002-9663-1557
2024-12-20T12:20:08Z
2024-12-20T12:20:08Z
2020
http://hdl.handle.net/10259/9825
10.71486/6whg-5774
The variable selection problem in the context of Linear Regression for large databases is analysed. The problem consists in selecting a small subset of independent variables that can perform the prediction task optimally. This problem has a wide range of applications. One important type of application is the design of composite indicators in various areas (sociology and economics, for example). Other important applications of variable selection in linear regression can be found in fields such as chemometrics, genetics, and climate prediction, among many others. For this problem, we propose a Branch & Bound method. This is an exact method and therefore guarantees optimal solutions. We also provide strategies that enable this method to be applied in very large databases (with hundreds of thousands of cases) in a moderate computation time. A series of computational experiments shows that our method performs well compared with well-known methods in the literature and with commercial software.
This work was partially supported by FEDER funds and the Spanish Ministry of Economy and Competitiveness (Projects ECO2016-76567-C4-2-R and PID2019-104263RB-C44), the Regional Government of “Castilla y León”, Spain (Project BU329U14 and BU071G19), the Regional Government of “Castilla y León” and FEDER funds (Project BU062U16 and COV2000375).
application/vnd.openxmlformats-officedocument.wordprocessingml.document
application/zip
text/plain
eng
Universidad de Burgos
http://hdl.handle.net/10259/8437
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ECO2016-76567-C4-2-R/ES/BÚSQUEDA DE LA EFICIENCIA Y SOSTENIBILIDAD DE LAS DECISIONES PÚBLICAS: UN ENFOQUE MULTICRITERIO/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104263RB-C44/ES/MEJORA EN LA TOMA DE DECISIONES EN EL AMBITO DE LA LOGISTICA Y PROBLEMAS RELACIONADOS. ENFOQUE MULTI-OBJETIVO/
info:eu-repo/grantAgreement/Junta de Castilla y León//BU329U14//Diseño de técnicas metaheurísticas para la toma de decisiones problemas con múltiples objetivos. Aplicaciones a problemas relacionados con transporte público y recogida de residuos/
info:eu-repo/grantAgreement/Junta de Castilla y León//BU071G19//Métodos heurísticos para problemas de optimización de recursos sanitarios con varios objetivos/
info:eu-repo/grantAgreement/Junta de Castilla y León//BU062U16//Metaheurísticas e hiperheurísticas para problemas de transporte público con varios criterios. Aplicaciones a problemas logísticos relacionados/
info:eu-repo/grantAgreement/Junta de Castilla y León//COV2000375//Predicción y evolución de casos UCI: Aplicación a la provincia de Burgos/
Atribución-NoComercial 4.0 Internacional
http://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
Variable selection
Linear regression
Branch & Bound methods
Heuristics
Investigación operativa
Bases de datos
Operations research
Databases
Dataset of the paper “Variable selection for linear regression in large databases: exact methods” Applied Intelligence, 51(6), 3736-3756
dataset
info:eu-repo/semantics/acceptedVersion
2020
LICENSE
license.txt
license.txt
text/plain; charset=utf-8
999
https://riubu.ubu.es/bitstream/10259/9825/6/license.txt
b295bcbce42e2caabeb0c623d3860c06
MD5
6
CC-LICENSE
license_rdf
license_rdf
application/rdf+xml; charset=utf-8
914
https://riubu.ubu.es/bitstream/10259/9825/5/license_rdf
24013099e9e6abb1575dc6ce0855efd5
MD5
5
ORIGINAL
readme.docx
readme.docx
application/vnd.openxmlformats-officedocument.wordprocessingml.document
14514
https://riubu.ubu.es/bitstream/10259/9825/1/readme.docx
e0d30e6fc1f01aebc66daff6446b027d
MD5
1
Pacheco-matrices_for_paper_variables_selection_in_linear_regression_size_n_18-42-2020.zip
Pacheco-matrices_for_paper_variables_selection_in_linear_regression_size_n_18-42-2020.zip
application/zip
89109911
https://riubu.ubu.es/bitstream/10259/9825/2/Pacheco-matrices_for_paper_variables_selection_in_linear_regression_size_n_18-42-2020.zip
8cb4578228ba42ecfc70036734474426
MD5
2
Pacheco-matrices_for_paper_variables_selection_in_linear_regression_size_n_45_-_60-2020.zip
Pacheco-matrices_for_paper_variables_selection_in_linear_regression_size_n_45_-_60-2020.zip
application/zip
101825921
https://riubu.ubu.es/bitstream/10259/9825/3/Pacheco-matrices_for_paper_variables_selection_in_linear_regression_size_n_45_-_60-2020.zip
613b61473570dec68b463b72900d441e
MD5
3
Pacheco-matrices_for_paper_variables_selection_in_linear_regression_size_n_63_-_72-2020.zip
Pacheco-matrices_for_paper_variables_selection_in_linear_regression_size_n_63_-_72-2020.zip
application/zip
86659961
https://riubu.ubu.es/bitstream/10259/9825/4/Pacheco-matrices_for_paper_variables_selection_in_linear_regression_size_n_63_-_72-2020.zip
53bac1a2542fb8a34d4f81df8a2bdf44
MD5
4
10259/9825
oai:riubu.ubu.es:10259/9825
2025-05-12 08:34:04.656
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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