Summary of Combining Supervised Learning and Reinforcement Learning For Multi-label Classification Tasks with Partial Labels, by Zixia Jia et al.
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
by Zixia Jia, Junpeng Li, Shichuan Zhang, Anji Liu, Zilong Zheng
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to tackle the challenges of multi-label positive-unlabelled learning (MLPUL), where only a subset of positive classes is annotated. The authors introduce Mixture Learner for Partially Annotated Classification (MLPAC), an RL-based framework that combines the exploration ability of reinforcement learning and the exploitation ability of supervised learning. Experimental results demonstrate the effectiveness of this framework across various tasks, including document-level relation extraction, multi-label image classification, and binary PU learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to learn from incomplete data. Right now, we rely on people annotating datasets for us to train our machine learning models. But what if some tasks are too hard or require too much expertise? The authors came up with an idea called MLPAC that uses both exploration (trying new things) and exploitation (making the most of what you have). They tested it on different tasks like identifying relationships in documents, classifying images, and binary classification. The results show that this approach is effective and can be used for many different problems. |
Keywords
» Artificial intelligence » Classification » Image classification » Machine learning » Reinforcement learning » Supervised
