Data integration in machine learning

DOIResolve DOI: http://doi.org/10.1109/BIBM.2015.7359925
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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 number23000089
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Record identifier57cbbc35-c458-4c37-9688-c36c2fd2c6c2
Record created2016-06-02
Record modified2016-06-02
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