A multi-view two-level classification method for generalized multi-instance problems

DOIResolve DOI: http://doi.org/10.1109/BigData.2014.7004363
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Proceedings title2014 IEEE International Conference on Big Data (Big Data)
Conference2nd IEEE International Conference on Big Data, IEEE Big Data 2014, October 27-30, 2014, Washington, DC, USA
Article number7004363
Pages104111; # of pages: 8
SubjectBig 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
AbstractMulti-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.
Publication date
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number21275640
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Record identifier85e659c5-7de8-4d10-a9fa-56eb8c25f143
Record created2015-07-14
Record modified2016-08-18
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