Label-sensitive task grouping by Bayesian nonparametric approach for multi-task multi-label learning
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conference contribution
posted on 2018-01-01, 00:00 authored by X Zhang, W Li, Tien Vu Nguyen, F Zhuang, H Xiong, S Lu© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Multi-label learning is widely applied in many real-world applications, such as image and gene annotation. While most of the existing multi-label learning models focus on the single-task learning problem, there are always some tasks that share some commonalities, which can help each other to improve the learning performances if the knowledge in the similar tasks can be smartly shared. In this paper, we propose a LABel-sensitive TAsk Grouping framework, named LABTAG, based on Bayesian nonparametric approach for multi-task multi-label classification. The proposed framework explores the label correlations to capture feature-label patterns, and clusters similar tasks into groups with shared knowledge, which are learned jointly to produce a strengthened multi-task multi-label model. We evaluate the model performance on three public multi-task multi-label data sets, and the results show that LABTAG outperforms the compared baselines with a significant margin.
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Event
Artificial Intelligence. International Joint Conference (27th : 2018 : Stockholm, Sweden)Pagination
3125 - 3131Publisher
International Joint Conferences on Artifical IntelligenceLocation
Stockholm, SwedenPlace of publication
Vienna, AustriaStart date
2018-07-13End date
2018-07-19ISSN
1045-0823ISBN-13
9780999241127Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, International Joint Conferences on Artificial IntelligenceEditor/Contributor(s)
J LangTitle of proceedings
IJCAI 2018: Proceedings of the 27th International Joint Conference on Artificial IntelligenceUsage metrics
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