National Research Council of Canada. Information and Communication Technologies
2nd IEEE International Conference on Big Data, IEEE Big Data 2014, October 27-30, 2014, Washington, DC, USA
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
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.
2014 IEEE International Conference on Big Data (Big Data), 7004363 (27 October 2014): 104–111.