# Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels Source: [Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:57:11.769Z Authors: Zixia Jia, Junpeng Li, Shichuan Zhang, Anji Liu, Zilong Zheng Published: 2024-06-24T03:36:19.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()