JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (5): 1-11.doi: 10.6040/j.issn.1671-9352.7.2023.239

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Multi-label learning based on granular neural networks

CHEN Yumin1, ZHENG Guangyu1*, JIAO Na2   

  1. 1. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, Fujian, China;
    2. School of Criminal Law, East China University of Political Science and Law, Shanghai 201620, China
  • Published:2024-05-09

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Abstract: This paper introduces the theory of granular computing and proposes a multi-label learning method based on granular neural networks. This method utilizes similarity granulation to capture the structural correlations in the data. Samples are granulated into granules on individual features, and granules across multiple features form granule vectors. Operations on granules and granule vectors are defined. On this basis, a granular loss function is introduced and a granular neural network is constructed for multi-label learning. Experiments are conducted on multiple Mulan multi-label datasets and compared with existing multi-label classification algorithms across various evaluation metrics. The results demonstrate the effectiveness and feasibility of the granular neural network multi-label learning algorithm.

Key words: granular computing, deep learning, granular neural networks, multi-label learning, granule vectors

CLC Number: 

  • TP181
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