| DOI | Resolve DOI: https://doi.org/10.1109/BigData.2014.7004363 |
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| Author | Search for: Wang, Xiaoguang; Search for: Liu, Xuan; Search for: Matwin, Stan; Search for: Japkowicz, Nathalie; Search for: Guo, Hongyu1 |
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| Name affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Conference | 2nd IEEE International Conference on Big Data, IEEE Big Data 2014, October 27-30, 2014, Washington, DC, USA |
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| Subject | Big data; Unsupervised learning; Classification framework; Classification methods; Empirical studies; Learning methods; Multi-instance problems; Multi-view learning; Multi-views; Supervised and unsupervised learning; Learning systems |
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| Abstract | Multi-instance (MI) learning is different than standard propositional classification, as it uses a set of bags containing many instances as input. While the instances in each bag are not labeled, the bags themselves are, as positive or negative. In this paper, we present a novel multi-view, two-level classification framework to address the generalized multi-instance problems. We first apply supervised and unsupervised learning methods to transform a MI dataset into a multi-view, single meta-instance dataset. Then we develop a multi-view learning approach that can integrate the information acquired by individual view learners on the meta-instance dataset from the previous step, and construct a final model. Our empirical studies show that the proposed method performs well compared to other popular MI learning methods. |
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| Publication date | 2014-10-27 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21275640 |
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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 | 85e659c5-7de8-4d10-a9fa-56eb8c25f143 |
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| Record created | 2015-07-14 |
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| Record modified | 2020-04-22 |
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