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
Link To Document