skip to main content
10.1145/3583780.3614912acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy

Published: 21 October 2023 Publication History

Abstract

Hierarchical multi-label classification aims at learning a multi-label classifier from a dataset whose labels are organized into a hierarchical structure. To the best of our knowledge, we propose for the first time the problem of finding a multi-label classifier given a partially labeled hierarchical multi-label dataset. We also assume the situation where the classifier cannot access hierarchical information during training. This work proposes an iterative framework for learning both multi-labels and a hierarchical structure of classes. When training a multi-label classifier from partial labels, our model extracts a class hierarchy from the classifier output using our hierarchy extraction algorithm. Then, our proposed loss exploits the extracted hierarchy to train the classifier. Theoretically, we show that our hierarchy extraction algorithm correctly finds the unknown hierarchy under a mild condition, and we prove that our loss function of multi-label classification with such hierarchy becomes an unbiased estimator of true multi-label classification risk. Our experiments show that our model obtains a class hierarchy close to the ground-truth dataset hierarchy, and simultaneously, our method outperforms previous methods for hierarchical multi-label classification and multi-label classification from partial labels.

References

[1]
Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. 2017. A closer look at memorization in deep networks. In International conference on machine learning. PMLR, 233--242.
[2]
Ricardo Cerri, Rodrigo C Barros, André C PLF de Carvalho, and Yaochu Jin. 2016. Reduction strategies for hierarchical multi-label classification in protein function prediction. BMC bioinformatics, Vol. 17, 1 (2016), 1--24.
[3]
Elijah Cole, Oisin Mac Aodha, Titouan Lorieul, Pietro Perona, Dan Morris, and Nebojsa Jojic. 2021. Multi-label learning from single positive labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 933--942.
[4]
Thibaut Durand, Nazanin Mehrasa, and Greg Mori. 2019. Learning a deep convnet for multi-label classification with partial labels. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 647--657.
[5]
Eleonora Giunchiglia and Thomas Lukasiewicz. 2020. Coherent hierarchical multi-label classification networks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9662--9673.
[6]
Yuhong Guo and Suicheng Gu. 2011. Multi-label classification using conditional dependency networks. In Twenty-Second International Joint Conference on Artificial Intelligence.
[7]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. CVPR. 2016. arXiv preprint arXiv:1512.03385 (2016).
[8]
Mengying Hu, Hu Han, Shiguang Shan, and Xilin Chen. 2018. Multi-label learning from noisy labels with non-linear feature transformation. In Asian Conference on Computer Vision. Springer, 404--419.
[9]
Dat Huynh and Ehsan Elhamifar. 2020. Interactive multi-label cnn learning with partial labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9423--9432.
[10]
Liping Jing, Liu Yang, Jian Yu, and Michael K Ng. 2015. Semi-supervised low-rank mapping learning for multi-label classification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1483--1491.
[11]
Youngwook Kim, Jae Myung Kim, Zeynep Akata, and Jungwoo Lee. 2022. Large Loss Matters in Weakly Supervised Multi-Label Classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14156--14165.
[12]
Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Andreas Veit, et al. 2017. Openimages: A public dataset for large-scale multi-label and multi-class image classification. Dataset available from https://github. com/openimages, Vol. 2, 3 (2017), 18.
[13]
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 2013. 3D Object Representations for Fine-Grained Categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13). Sydney, Australia.
[14]
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. [n.,d.]. CIFAR-100 (Canadian Institute for Advanced Research). ( [n.,d.]). http://www.cs.toronto.edu/ kriz/cifar.html
[15]
Shailesh Kumar, Joydeep Ghosh, and Melba M Crawford. 2002. Hierarchical fusion of multiple classifiers for hyperspectral data analysis. Pattern Analysis & Applications, Vol. 5, 2 (2002), 210--220.
[16]
Kaustav Kundu and Joseph Tighe. 2020. Exploiting weakly supervised visual patterns to learn from partial annotations. Advances in Neural Information Processing Systems, Vol. 33 (2020), 561--572.
[17]
Guofa Li, Zefeng Ji, Yunlong Chang, Shen Li, Xingda Qu, and Dongpu Cao. 2021. ML-ANet: A transfer learning approach using adaptation network for multi-label image classification in autonomous driving. Chinese Journal of Mechanical Engineering, Vol. 34, 1 (2021), 1--11.
[18]
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, Jesse Berent, Abhinav Gupta, Rahul Sukthankar, and Luc Van Gool. 2017. Webvision challenge: Visual learning and understanding with web data. arXiv preprint arXiv:1705.05640 (2017).
[19]
Yi Liu, Rong Jin, and Liu Yang. 2006. Semi-supervised multi-label learning by constrained non-negative matrix factorization. In AAAi, Vol. 6. 421--426.
[20]
Emir Mu noz, V'it Novávc ek, and Pierre-Yves Vandenbussche. 2019. Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models. Briefings in bioinformatics, Vol. 20, 1 (2019), 190--202.
[21]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019).
[22]
Dhruvesh Patel, Pavitra Dangati, Jay-Yoon Lee, Michael Boratko, and Andrew McCallum. 2021. Modeling label space interactions in multi-label classification using box embeddings. In International Conference on Learning Representations.
[23]
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. 2015. Imagenet large scale visual recognition challenge. International journal of computer vision, Vol. 115, 3 (2015), 211--252.
[24]
Grigorios Tsoumakas and Ioannis Katakis. 2007. Multi-label classification: An overview. International Journal of Data Warehousing and Mining (IJDWM), Vol. 3, 3 (2007), 1--13.
[25]
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge Belongie. 2017. Learning from noisy large-scale datasets with minimal supervision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 839--847.
[26]
Celine Vens, Jan Struyf, Leander Schietgat, Savs o Dvz eroski, and Hendrik Blockeel. 2008. Decision trees for hierarchical multi-label classification. Machine learning, Vol. 73, 2 (2008), 185--214.
[27]
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. 2011. The caltech-ucsd birds-200--2011 dataset. (2011).
[28]
Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu. 2016. Cnn-rnn: A unified framework for multi-label image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2285--2294.
[29]
Lichen Wang, Yunyu Liu, Can Qin, Gan Sun, and Yun Fu. 2020. Dual relation semi-supervised multi-label learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 6227--6234.
[30]
Jonatas Wehrmann, Ricardo Cerri, and Rodrigo Barros. 2018. Hierarchical multi-label classification networks. In International conference on machine learning. PMLR, 5075--5084.
[31]
CF Jeff Wu. 1983. On the convergence properties of the EM algorithm. The Annals of statistics (1983), 95--103.
[32]
Ming-Kun Xie and Sheng-Jun Huang. 2018. Partial multi-label learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.
[33]
Ning Xu, Yun-Peng Liu, and Xin Geng. 2020. Partial multi-label learning with label distribution. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 6510--6517.
[34]
Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, and Min-Ling Zhang. 2022. One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement. arXiv preprint arXiv:2206.00517 (2022).
[35]
Yong Zheng, Bamshad Mobasher, and Robin Burke. 2014. Context recommendation using multi-label classification. In 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Vol. 2. IEEE, 288--295.
[36]
Donghao Zhou, Pengfei Chen, Qiong Wang, Guangyong Chen, and Pheng-Ann Heng. 2022. Acknowledging the unknown for multi-label learning with single positive labels. In Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXIV. Springer, 423--440.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. hierarchical multi-label classification
  2. multi-label classification
  3. multi-label learning from partial labels
  4. semi-supervised learning
  5. weakly supervised learning

Qualifiers

  • Research-article

Funding Sources

  • National Research Foundation of Korea(NRF)

Conference

CIKM '23
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 327
    Total Downloads
  • Downloads (Last 12 months)177
  • Downloads (Last 6 weeks)4
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media