Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning
Abstract
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.
Cite
Text
Zhang et al. "Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/434Markdown
[Zhang et al. "Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/zhang2018ijcai-label/) doi:10.24963/IJCAI.2018/434BibTeX
@inproceedings{zhang2018ijcai-label,
title = {{Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning}},
author = {Zhang, Xiao and Li, Wenzhong and Nguyen, Vu and Zhuang, Fuzhen and Xiong, Hui and Lu, Sanglu},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {3125-3131},
doi = {10.24963/IJCAI.2018/434},
url = {https://mlanthology.org/ijcai/2018/zhang2018ijcai-label/}
}