Skip to main content
SpringerLink
Log in

Bi-directional mapping for multi-label learning of label-specific features

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In multi-label learning, scholars have proposed many multi-label learning algorithms that explore label-specific features in recent years. Previous studies tend to focus only on the forward projection of the instance feature space to the category label space to learn label-specific features for multi-label classification, and only simple correlations between labels are considered; however, the loss of discriminative information in the instance space and the essential connections between labels resulting from the reduction of feature dimensionality during forward projection are usually ignored. Based on the overall consideration, in this paper, we propose a bi-directional mapping for multi-label learning of label-specific features method(BDLS). Specifically, under a unified linear model for learning label-specific features for multi-label classification, we propose a novel reconstruction loss function to compensate for the loss of discriminative information generated during forward mapping. And we also propose an effective causal learning machine to explore the intrinsic causal relationships among labels for the purpose of mining the essential connections among labels. Experimental results and analysis on several multi-label datasets validate the effectiveness of our proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. code:http://mulan.sourceforge.net/datasets-mlc.html.

  2. code:http://www.escience.cn/people/huangjun/index.html.

  3. code:http://palm.seu.edu.cn/zhangml.

  4. code:http://palm.seu.edu.cn/zhangml.

  5. code:http://www.escience.cn/people/huangjun/index.html.

References

  1. Tsoumakas G, Katakis I (2007) Multi-label classification: An overview. Int J Data Warehous Min (IJDWM) 3(3):1–13

    Article  Google Scholar 

  2. Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819– 1837

    Article  Google Scholar 

  3. Ueda N, Saito K (2003) Parametric mixture models for multi-labeled text. In: Advances in neural information processing systems, pp 737–744

  4. Schapire RE, Boostexter YS (2000) A boosting-based system for text categorization. Mach Learn 39(2):135–168

    Article  Google Scholar 

  5. Qi G-J, Hua X-S, Rui Y, Tang J, Mei T, Zhang H-J (2007) Correlative multi-label video annotation. In: Proceedings of the 15th ACM international conference on Multimedia, pp 17–26

  6. Barutcuoglu Z, Schapire RE, Troyanskaya OG (2006) Hierarchical multi-label prediction of gene function. Bioinformatics 22(7):830–836

    Article  Google Scholar 

  7. Trohidis K, Tsoumakas G, Kalliris G, Vlahavas IP (2008) Multi-label classification of music into emotions. ISMIR 8:325–330

    Google Scholar 

  8. Wu B, Zhong E, Horner A, Yang Q (2014) Music emotion recognition by multi-label multi-layer multi-instance multi-view learning. In: Proceedings of the 22nd ACM international conference on Multimedia, pp 117–126

  9. Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771

    Article  Google Scholar 

  10. Zhang M-L, Zhou Z-H (2007) Ml-knn: A lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  Google Scholar 

  11. Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surv (CSUR) 47(3):1–38

    Article  Google Scholar 

  12. Han H, Huang M, Yu Z, Yang X, Feng W (2019) Multi-label learning with label specific features using correlation information. IEEE Access 7:11474–11484

    Article  Google Scholar 

  13. Zhang M-L, Wu L (2014) Lift: Multi-label learning with label-specific features. IEEE Trans Pattern Anal Mach Intell 37(1):107–120

    Article  Google Scholar 

  14. Ma Z, Nie F, Yi Y, Uijlings JRR, Sebe N (2012) Web image annotation via subspace-sparsity collaborated feature selection. IEEE Trans Multimed 14(4):1021–1030

    Article  Google Scholar 

  15. Jian L, Li J, Shu K, Liu H (2016) Multi-label informed feature selection. IJCAI 16:1627–33

    Google Scholar 

  16. Chang X, Nie F, Yi Y, Huang H (2014) A convex formulation for semi-supervised multi-label feature selection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 28

  17. Li J, Zhang C, Zhu P, Wu B, Chen L, Hu Q (2020) Spl-mll: Selecting predictable landmarks for multi-label learning. In: European Conference on Computer Vision. Springer, pp 783–799

  18. Huang J, Li G, Huang Q, Wu X (2017) Joint feature selection and classification for multilabel learning. IEEE Trans Cybern 48(3):876–889

    Article  Google Scholar 

  19. Huang J, Li G, Huang Q, Wu X (2016) Learning label-specific features and class-dependent labels for multi-label classification. IEEE Trans Knowl Data Eng 28(12):3309–3323

    Article  Google Scholar 

  20. Pearl J, Mackenzie D (2018) The book of why: the new science of cause and effect. Basic books

  21. Gibaja E, Ventura S (2014) Multi-label learning: a review of the state of the art and ongoing research. Wiley Interdiscip Rev Data Min Knowl Discov 4(6):411–444

    Article  Google Scholar 

  22. Tsoumakas G, Vlahavas I (2007) Random k-labelsets: An ensemble method for multilabel classification. In: European conference on machine learning. Springer, pp 406–417

  23. Read J, Pfahringer B, Holmes G (2008) Multi-label classification using ensembles of pruned sets. In: 2008 eighth IEEE international conference on data mining. IEEE, pp 995–1000

  24. Hüllermeier E, Fürnkranz J, Cheng W, Brinker K (2008) Label ranking by learning pairwise preferences. Artif Intell 172(16-17):1897–1916

    Article  MathSciNet  Google Scholar 

  25. Fürnkranz J, Hüllermeier E, Loza mencía E, Brinker K (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133–153

    Article  Google Scholar 

  26. Zhang M-L, Zhou Z-H (2007) Multi-label learning by instance differentiation. AAAI 7:669–674

    Google Scholar 

  27. Elisseeff A, Weston J, et al. (2001) A kernel method for multi-labelled classification. NIPS 14:681–687

    Google Scholar 

  28. Clare A, King RD (2001) Knowledge discovery in multi-label phenotype data. In: European conference on principles of data mining and knowledge discovery. Springer, pp 42–53

  29. Zhang M-L, Zhou Z-H (2006) Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 18(10):1338–1351

    Article  Google Scholar 

  30. Cheng W, Hüllermeier E (2009) Combining instance-based learning and logistic regression for multilabel classification. Mach Learn 76(2-3):211–225

    Article  Google Scholar 

  31. Tsoumakas G, Katakis T, Vlahavas T (2009) Mining multi-label data. In: Data mining and knowledge discovery handbook. Springer, pp 667–685

  32. Zhang M-L, Zhang K (2010) Multi-label learning by exploiting label dependency. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 999–1008

  33. Liu H, Li X, Zhang S (2016) Learning instance correlation functions for multilabel classification. IEEE Trans Cybern 47(2):499–510

    Article  Google Scholar 

  34. Gong C, Tao D, Yang J, Liu W (2016) Teaching-to-learn and learning-to-teach for multi-label propagation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 30

  35. Zhu S, Ji X, Xu W, Gong Y (2005) Multi-labelled classification using maximum entropy method. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp 274–281

  36. Li Y-K, Zhang M-L, Geng X (2015) Leveraging implicit relative labeling-importance information for effective multi-label learning. In: IEEE International Conference on Data Mining. IEEE, pp 251–260

  37. Godbole S, Sarawagi S (2004) Discriminative methods for multi-labeled classification. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 22–30

  38. Yan R, Tesic J, Smith JR (2007) Model-shared subspace boosting for multi-label classification. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 834–843

  39. Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333

    Article  MathSciNet  Google Scholar 

  40. Charte F, Rivera AJ, Del Jesus MJ, Herrera F (2014) Li-mlc: a label inference methodology for addressing high dimensionality in the label space for multilabel classification. IEEE Trans Neural Netw Learn Syst 25(10):1842–1854

    Article  Google Scholar 

  41. Zhang J-J, Fang M, Li X (2015) Multi-label learning with discriminative features for each label. Neurocomputing 154:305–316

    Article  Google Scholar 

  42. Guo Y, Chung F, Li G, Wang J, Gee JC (2019) Leveraging label-specific discriminant mapping features for multi-label learning. ACM Trans Knowl Discov Data (TKDD) 13(2):1–23

    Article  Google Scholar 

  43. Sun L, Kudo M, Kimura K (2016) Multi-label classification with meta-label-specific features. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, pp 1612– 1617

  44. Huang J, Li G, Huang Q, Wu X (2015) Learning label specific features for multi-label classification. In: 2015 IEEE International Conference on Data Mining. IEEE, pp 181–190

  45. He Z-F, Yang M (2019) Sparse and low-rank representation for multi-label classification. Appl Intell 49(5):1708–1723

    Article  Google Scholar 

  46. Huang J, Qin F, Zheng X, Cheng Z, Yuan Z, Zhang W, Huang Q (2019) Improving multi-label classification with missing labels by learning label-specific features. Inf Sci 492:124–146

    Article  MathSciNet  Google Scholar 

  47. Cheng Y, Zhao D, Wang Y, Pei G (2019) Multi-label learning with kernel extreme learning machine autoencoder. Knowl-Based Syst 178:1–10

    Article  Google Scholar 

  48. Ling Z, Yu K, Zhang Y, Liu L, Li J (2021) Causal learner: A toolbox for causal structure and markov blanket learning. arXiv:2103.06544

  49. Margaritis D, Thrun S (1999) Bayesian network induction via local neighborhoods. Technical report, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE

  50. Yang S, Wang H, Yu K, Cao F, Wu X (2019) Towards efficient local causal structure learning. arXiv:1910.01288

  51. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202

    Article  MathSciNet  Google Scholar 

  52. Wang Y, Zheng W, Cheng Y, Zhao D (2020) Joint label completion and label-specific features for multi-label learning algorithm. Soft Comput 24(9):6553–6569

    Article  Google Scholar 

  53. Bucak SS, Jin R, Jain AK (2011) Multi-label learning with incomplete class assignments. In: CVPR 2011. IEEE, pp 2801–2808

  54. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 62071001), the Anhui Natural Science Foundation of China (Nos. 2008085MF192 and 2008085MF183), the Key Science Project of Anhui Education Department of China (Nos. KJ2018A0012, KJ2019A0023, and KJ2019A0022), and the CERNET Innovation Project of China (Nos. NGII20180612, NGII20180312, and NGII20180624).

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Automation, Anhui University, Hefei, China

    Yi Tan, Dong Sun, Yu Shi, Liuya Gao, Qingwei Gao & Yixiang Lu

Authors
  1. Yi Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liuya Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qingwei Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yixiang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dong Sun.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tan, Y., Sun, D., Shi, Y. et al. Bi-directional mapping for multi-label learning of label-specific features. Appl Intell 52, 8147–8166 (2022). https://doi.org/10.1007/s10489-021-02868-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02868-4

Keywords

Search

Navigation