Abstract
Multi-label classification refers to the supervised learning problem where an instance may be associated with multiple labels. It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically assume that the distribution of classes is balanced. In many real-world applications, multi-label datasets with imbalanced class distributions occur frequently, which may make various multi-label learning methods ineffective. Since the existing multi-label learning algorithms pay less attention to the problem of correlation with imbalanced label sets, this paper proposed a Multi-Label learning model by exploiting Imbalanced Label Correlations (ML-ILC). ML-ILC uses graph convolution neural network to learn the correlation between labels. At the same time, we suggest that the regularization of minority classes is stronger than that of frequent classes, which can improve the generalization error of minority classes. To investigate the performance of the proposed multi-label learning model, we considered two benchmark datasets including VOC2007 and COCO. The proposed method successfully achieved better classification performance compared to the state-of-the-art compression methods.
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Acknowledgments
This work is supported by NSFC under Grant 62076179, and Grant 61732011, Beijing Natural Science Foundation under Grant Z180006, Tianjin Science and Technology Plan Project under Grant 19ZXZNGX00050, Funded by Open Research Fund of the Public Security Behavioral Science Laboratory, People’s Public Security University of China under Grant 2021SYS02.
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Gu, S., Yang, L., Li, Y., Li, H. (2021). Multi-label Learning by Exploiting Imbalanced Label Correlations. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_44
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