Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911930480852992 |
|---|---|
| author | Jia, Zixia Li, Junpeng Zhang, Shichuan Liu, Anji Zheng, Zilong |
| author_facet | Jia, Zixia Li, Junpeng Zhang, Shichuan Liu, Anji Zheng, Zilong |
| contents | Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like document-level relation extraction, pose challenges in fully manual annotation due to the specific domain knowledge and large class sets. Therefore, we address the multi-label positive-unlabelled learning (MLPUL) problem, where only a subset of positive classes is annotated. We propose Mixture Learner for Partially Annotated Classification (MLPAC), an RL-based framework combining the exploration ability of reinforcement learning and the exploitation ability of supervised learning. Experimental results across various tasks, including document-level relation extraction, multi-label image classification, and binary PU learning, demonstrate the generalization and effectiveness of our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16293 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels Jia, Zixia Li, Junpeng Zhang, Shichuan Liu, Anji Zheng, Zilong Computation and Language Artificial Intelligence Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like document-level relation extraction, pose challenges in fully manual annotation due to the specific domain knowledge and large class sets. Therefore, we address the multi-label positive-unlabelled learning (MLPUL) problem, where only a subset of positive classes is annotated. We propose Mixture Learner for Partially Annotated Classification (MLPAC), an RL-based framework combining the exploration ability of reinforcement learning and the exploitation ability of supervised learning. Experimental results across various tasks, including document-level relation extraction, multi-label image classification, and binary PU learning, demonstrate the generalization and effectiveness of our framework. |
| title | Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.16293 |