2026-06-19T12:17:31Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/84372024-05-10T11:58:39Zcom_10259_5645com_10259_5086com_10259_2604col_10259_5646

Pacheco Bonrostro, Joaquín Casado Yusta, Silvia 2024-01-23T10:59:45Z 2024-01-23T10:59:45Z 2020-11 0924-669X http://hdl.handle.net/10259/8437 10.1007/s10489-020-01927-6 1573-7497 This paper analyzes the variable selection problem in the context of Linear Regression for large databases. The problem consists of 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 to 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/pdf eng Springer Applied Intelligence. 2021, V. 51, n. 6, p. 3736–3756 https://doi.org/10.1007/s10489-020-01927-6 Variable selection Linear regression Branch & Bound methods Heuristics Economía Matemáticas Economics Mathematics Variable selection for linear regression in large databases: exact methods info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess Applied Intelligence 51 6 3736 3756