RT dataset
T1 Dataset of the paper “Variable selection for linear regression in large databases: exact methods” Applied Intelligence, 51(6), 3736-3756
A1 Pacheco Bonrostro, Joaquín
A1 Casado Yusta, Silvia
K1 Variable selection
K1 Linear regression
K1 Branch & Bound methods
K1 Heuristics
K1 Investigación operativa
K1 Operations research
K1 Bases de datos
K1 Databases
AB 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.
PB Universidad de Burgos
YR 2020
FD 2020
LK http://hdl.handle.net/10259/9825
UL http://hdl.handle.net/10259/9825
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 02-ene-2025