Data integration in machine learning

DOIResolve DOI: http://doi.org/10.1109/BIBM.2015.7359925
AuthorSearch for: ; Search for:
TypeArticle
Proceedings title2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Conference2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), November 9-12, 2015, Washington, DC, USA
ISBN978-1-4673-6799-8
Pages16651671
Subjectdata integration; Bayesian network; decision tree; random forest; multiple kernel learning; feature extraction; deep learning
AbstractModern 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.
Publication date
PublisherIEEE
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23000015
Export citationExport as RIS
Report a correctionReport a correction
Record identifierb505f592-59d6-41fb-ad11-24604539569f
Record created2016-05-19
Record modified2016-05-24
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)