Abstract
Partial Multi-label Learning is a multi-label classification problem where only candidate labels are given for training data. These candidate labels consist of relevant labels and false-positive labels. In this paper, we consider the PML when a few accurately labeled data are available. In practice, it is difficult to remove false-positive labels fully due to a large cost, but it is possible to do that in a few instances with a smaller cost. Conventional PML methods do not assume those accurately labeled data so it is hard to utilize data effectively. We propose a new algorithm called PML-VD to utilize those accurately labeled data. PML-VD first disambiguates the noisy-labeled data with both accurately labeled data and noisy labeled data and then learns a classifier. This two-stage approach enables the effective utilization of accurately labeled data without overfitting. Experiments on nine PML datasets shows the effectiveness of explicit utilization of accurately labeled data. In best cases, PML-VD improves 7% classification accuracy in terms of ranking loss.
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Notes
- 1.
Due to the space limitation, we report the detail only with 1% and 3% validation sets.
- 2.
PML-VD also has another parameter k but the effect is small and thus omitted due to the space limitation.
References
Chen, Z.M., Wei, X.S., Wang, P., Guo, Y.: Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: De Raedt, L., Siebes, A. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44794-6_4
Huiskes, M.J., Lew, M.S.: The MIR flickr retrieval evaluation. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 39–43 (2008)
Lawson, C.L., Hanson, R.J.: Solving Least Squares Problems. Society for Industrial and Applied Mathematics (1995). https://doi.org/10.1137/1.9781611971217
Lyu, G., Feng, S., Li, Y.: Noisy label tolerance: a new perspective of partial multi-label learning. Inf. Sci. 543, 454–466 (2021)
Read, J., Perez-Cruz, F.: Deep learning for multi-label classification. arXiv preprint: arXiv:1502.05988 (2014)
Schapire, R.E., Singer, Y.: BoosTexter: a boosting-based system for text categorization. Mach. Learn. 39(2), 135–168 (2000)
Xie, M.K., Huang, S.J.: Partial multi-label learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)
Xie, M.K., Huang, S.J.: Partial multi-label learning with noisy label identification. IEEE Trans. Pattern Anal. Mach. Intell. 44, 3676–3687 (2021)
Xie, M.K., Sun, F., Huang, S.J.: Partial multi-label learning with meta disambiguation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1904–1912 (2021)
Zhang, M.L., Fang, J.P.: Partial multi-label learning via credible label elicitation. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3587–3599 (2020)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2013)
Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine learning (ICML-03), pp. 912–919 (2003)
Acknowledgment
This work was partially supported by JSPS KAKENHI Grant Number 19H04128.
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Mizuguchi, H., Kimura, K., Kudo, M., Sun, L. (2024). Partial Multi-label Learning with a Few Accurately Labeled Data. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14326. Springer, Singapore. https://doi.org/10.1007/978-981-99-7022-3_7
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DOI: https://doi.org/10.1007/978-981-99-7022-3_7
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