National Research Council of Canada. Information and Communication Technologies
2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), November 9-12, 2015, Washington, DC, USA
data integration; Bayesian network; decision tree; random forest; multiple kernel learning; feature extraction; deep learning
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.
2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM): 1665–1671.