2026-06-17T21:54:39Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/98252025-05-12T06:34:04Zcom_10259_5645com_10259_5086com_10259_2604col_10259_9820col_10259_5684
Repositorio Institucional de la Universidad de Burgos
author
Pacheco Bonrostro, Joaquín
author
Casado Yusta, Silvia
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.
eng
Atribución-NoComercial 4.0 Internacional
Variable selection
Linear regression
Branch & Bound methods
Heuristics
Dataset of the paper “Variable selection for linear regression in large databases: exact methods” Applied Intelligence, 51(6), 3736-3756
dataset
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