Multi-label test classification based on capsule network and virtual adversarial training

Ziqiang Zhong; Yaxin Li

doi:10.1117/12.2684897

19 October 2023 Multi-label test classification based on capsule network and virtual adversarial training

Ziqiang Zhong, Yaxin Li

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127092J (2023) https://doi.org/10.1117/12.2684897
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Based on capsule network and virtual adversarial training, a multi-label text classification method is proposed for the multi-label text classification problem to solve the problems of insufficient feature extraction and weak model robustness. This method used BERT model to obtain the word vector representation of the sample and used the dynamic routing mechanism of capsule network to extract text features. At the same time, the method added disturbance construction adversarial samples into the input samples, and updated the model parameters by virtual adversarial training method, finally, improved robustness and classification effect of the model. Experiments on AAPD and RCV1-V2 datasets show that the micro-F1 value is better than those of other benchmark models. Experimental results show that the proposed method can be effectively applied to multi-label text classification tasks.

Citation Download Citation

Ziqiang Zhong and Yaxin Li "Multi-label test classification based on capsule network and virtual adversarial training", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127092J (19 October 2023); https://doi.org/10.1117/12.2684897

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