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Label-sensitive task grouping by Bayesian nonparametric approach for multi-task multi-label learning

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.

History

Event

Artificial Intelligence. International Joint Conference (27th : 2018 : Stockholm, Sweden)

Pagination

3125 - 3131

Publisher

International Joint Conferences on Artifical Intelligence

Location

Stockholm, Sweden

Place of publication

Vienna, Austria

Start date

2018-07-13

End date

2018-07-19

ISSN

1045-0823

ISBN-13

9780999241127

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2018, International Joint Conferences on Artificial Intelligence

Editor/Contributor(s)

J Lang

Title of proceedings

IJCAI 2018: Proceedings of the 27th International Joint Conference on Artificial Intelligence

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