2026-06-17T20:29:47Zhttps://riubu.ubu.es/oai/request

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

00925njm 22002777a 4500 dc Pacheco Bonrostro, Joaquín author Casado Yusta, Silvia author 2020-11 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. 0924-669X http://hdl.handle.net/10259/8437 10.1007/s10489-020-01927-6 1573-7497 Variable selection Linear regression Branch & Bound methods Heuristics Variable selection for linear regression in large databases: exact methods