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 :
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