Universidad de Burgos RIUBU Principal Default Universidad de Burgos RIUBU Principal Default
  • español
  • English
  • français
  • Deutsch
  • português (Brasil)
  • italiano
Universidad de Burgos RIUBU Principal Default
  • Ayuda
  • Kontakt
  • Feedback abschicken
  • Acceso abierto
    • Archivar en RIUBU
    • Acuerdos editoriales para la publicación en acceso abierto
    • Controla tus derechos, facilita el acceso abierto
    • Sobre el acceso abierto y la UBU
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Stöbern

    Gesamter BestandBereiche & SammlungenErscheinungsdatumAutorenTitelnSchlagwortenDiese SammlungErscheinungsdatumAutorenTitelnSchlagworten

    Mein Benutzerkonto

    EinloggenRegistrieren

    Statistiken

    Benutzungsstatistik

    Compartir

    Dokumentanzeige 
    •   RIUBU Startseite
    • E-Prints
    • Untitled
    • Untitled
    • Untitled
    • Dokumentanzeige
    •   RIUBU Startseite
    • E-Prints
    • Untitled
    • Untitled
    • Untitled
    • Dokumentanzeige

    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/8437

    Título
    Variable selection for linear regression in large databases: exact methods
    Autor
    Pacheco Bonrostro, JoaquínAutoridad UBU Orcid
    Casado Yusta, SilviaAutoridad UBU Orcid
    Publicado en
    Applied Intelligence. 2021, V. 51, n. 6, p. 3736–3756
    Editorial
    Springer
    Fecha de publicación
    2020-11
    ISSN
    0924-669X
    DOI
    10.1007/s10489-020-01927-6
    Zusammenfassung
    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.
    Palabras clave
    Variable selection
    Linear regression
    Branch & Bound methods
    Heuristics
    Materia
    Economía
    Economics
    Matemáticas
    Mathematics
    URI
    http://hdl.handle.net/10259/8437
    Versión del editor
    https://doi.org/10.1007/s10489-020-01927-6
    Aparece en las colecciones
    • Untitled
    Dateien zu dieser Ressource
    Nombre:
    Pacheco-ai_2021.pdf
    Tamaño:
    660.5Kb
    Formato:
    Adobe PDF
    Thumbnail
    Öffnen

    Métricas

    Citas

    Academic Search
    Ver estadísticas de uso

    Exportar

    RISMendeleyRefworksZotero
    • edm
    • marc
    • xoai
    • qdc
    • ore
    • ese
    • dim
    • uketd_dc
    • oai_dc
    • etdms
    • rdf
    • mods
    • mets
    • didl
    • premis
    Zur Langanzeige