RT info:eu-repo/semantics/article
T1 Variable selection for linear regression in large databases: exact methods
A1 Pacheco Bonrostro, Joaquín
A1 Casado Yusta, Silvia
K1 Variable selection
K1 Linear regression
K1 Branch & Bound methods
K1 Heuristics
K1 Economía
K1 Economics
K1 Matemáticas
K1 Mathematics
AB 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.
PB Springer
SN 0924-669X
YR 2020
FD 2020-11
LK http://hdl.handle.net/10259/8437
UL http://hdl.handle.net/10259/8437
LA eng
NO 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).
DS Repositorio Institucional de la Universidad de Burgos
RD 12-may-2024