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
         
        
        
        
        
            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"
         
        
        
            Conference_Titel : 
Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on
         
        
        
            DOI : 
10.1109/BIBM.2015.7359925