| Download | - View final version: Data integration in machine learning (PDF, 1.4 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1109/BIBM.2015.7359925 |
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| Author | Search for: Li, Yifeng1; Search for: Ngom, Alioune |
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| Affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Conference | 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), November 9-12, 2015, Washington, DC, USA |
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| Subject | data integration; Bayesian network; decision tree; random forest; multiple kernel learning; feature extraction; deep learning |
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| 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. |
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| Publication date | 2015-12-17 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23000015 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | b505f592-59d6-41fb-ad11-24604539569f |
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| Record created | 2016-05-19 |
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| Record modified | 2020-06-02 |
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