• DocumentCode
    3714620
  • Title

    Data integration in machine learning

  • Author

    Yifeng Li;Alioune Ngom

  • Author_Institution
    Information and Communications Technologies, National Research Council of Canada, Ottawa, Ontario, Canada
  • fYear
    2015
  • Firstpage
    1665
  • Lastpage
    1671
  • Abstract
    Modern data generated in many fields are in a strong need of integrative machine learning models in order to better make use of heterogeneous information in decision making and knowledge discovery. How data from multiple sources are incorporated in a learning system is key step for a successful analysis. In this paper, we provide a comprehensive review on data integration techniques from a machine learning perspective.
  • Keywords
    "Genomics","Bioinformatics","Yttrium","Lead","Loading"
  • Publisher
    ieee
  • Conference_Titel
    Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/BIBM.2015.7359925
  • Filename
    7359925