The Community for Technology Leaders
Green Image
Issue No. 06 - June (2018 vol. 30)
ISSN: 1041-4347
pp: 1065-1080
Jia Wu , Department of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia
Shirui Pan , Centre for Artificial Intelligence, University of Technology Sydney, Ultimo, NSW, Australia
Xingquan Zhu , Department of Computer and Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL
Chengqi Zhang , Centre for Artificial Intelligence, University of Technology Sydney, Ultimo, NSW, Australia
Xindong Wu , School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA
Multi-instance learning (MIL) is a useful tool for tackling labeling ambiguity in learning because it allows a bag of instances to share one label. Bag mapping transforms a bag into a single instance in a new space via instance selection and has drawn significant attention recently. To date, most existing work is based on the original space, using all instances inside each bag for bag mapping, and the selected instances are not directly tied to an MIL objective. As a result, it is difficult to guarantee the distinguishing capacity of the selected instances in the new bag mapping space. In this paper, we propose a discriminative mapping approach for multi-instance learning (MILDM) that aims to identify the best instances to directly distinguish bags in the new mapping space. Accordingly, each instance bag can be mapped using the selected instances to a new feature space, and hence any generic learning algorithm, such as an instance-based learning algorithm, can be used to derive learning models for multi-instance classification. Experiments and comparisons on eight different types of real-world learning tasks (including 14 data sets) demonstrate that MILDM outperforms the state-of-the-art bag mapping multi-instance learning approaches. Results also confirm that MILDM achieves balanced performance between runtime efficiency and classification effectiveness.
Algorithm design and analysis, Supervised learning, Training, Electronic mail, Vocabulary, Labeling

J. Wu, S. Pan, X. Zhu, C. Zhang and X. Wu, "Multi-Instance Learning with Discriminative Bag Mapping," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 6, pp. 1065-1080, 2018.
176 ms
(Ver 3.3 (11022016))