Skip to main content

Advertisement

Springer Nature Link
Log in

Fuzzy information gain ratio-based multi-label feature selection with label correlation

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Multi-label feature selection aims to mitigate the curse of dimensionality in multi-label data by selecting a smaller subset of features from the original set for classification. Existing multi-label feature selection algorithms frequently neglect the inherent uncertainty in multi-label data and fail to adequately consider the relationships between features and labels when assessing the importance of features. In response to this challenge, a Fuzzy Information Gain Ratio-based multi-label feature selection considering Label Correlation (FIGR_LC) algorithm is proposed. FIGR_LC evaluates feature importance by combining the relationship between features and individual labels, as well as the correlation between features and label sets. Subsequently, a feature ranking is established based on these feature weights. Experimental results substantiate the effectiveness of FIGR_LC, showcasing its superiority over several established feature selection methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Algorithm 1
Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The data that support the findings of this study are openly available in Mulan at https://mulan.sourceforge.net/.

References

  1. Mitchell TM, Mitchell TM (1997) Machine learning. McGraw-Hill, New York

    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

    Google Scholar 

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

    Google Scholar 

  4. Li L, Wang M, Zhang L, Wang H (2014) Learning semantic similarity for multi-label text categorization. In: Workshop on Chinese lexical semantics. Macao, China, pp 260–269

    Google Scholar 

  5. Jiang J-Y, Tsai S-C, Lee S-J (2012) Fsknn: multi-label text categorization based on fuzzy similarity and k nearest neighbors. Expert Syst Appl 39(3):2813–2821

    Google Scholar 

  6. Wang C, Yan S, Zhang L, Zhang H-J (2009) Multi-label sparse coding for automatic image annotation. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). FL, USA, Miami, pp 1643–1650

    Google Scholar 

  7. Wu B, Lyu S, Hu B-G, Ji Q (2015) Multi-label learning with missing labels for image annotation and facial action unit recognition. Pattern Recognit 48(7):2279–2289

    Google Scholar 

  8. Yu Y, Pedrycz W, Miao D (2013) Neighborhood rough sets based multi-label classification for automatic image annotation. Int J Approx Reason 54(9):1373–1387

    Google Scholar 

  9. Zhao F, Huang Y, Wang L, Tan T (2015) Deep semantic ranking based hashing for multi-label image retrieval. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). MA, USA, Boston, pp 1556–1564

    Google Scholar 

  10. Yu K, Yu S, Tresp V (2005) Multi-label informed latent semantic indexing. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '05). Salvador, Brazil, pp 258–265

    Google Scholar 

  11. Zhu C, Liu Y, Miao D, Dong Y, Pedrycz W (2023) within-cross-consensus-view representation-based multi-view multi-label learning with incomplete data. Neurocomputing 557:126729

    Google Scholar 

  12. Zhu C, Miao D, Wang Z, Zhou R, Wei L, Zhang X (2020) global and local multi-view multi-label learning. Neurocomputing 371:67–77

    Google Scholar 

  13. Lin Y, Hu Q, Liu J, Chen J, Duan J (2016) Multi-label feature selection based on neighborhood mutual information. Appl Soft Comput 38:244–256

    Google Scholar 

  14. Li L, Liu H, Ma Z, Mo Y, Duan Z, Zhou J, Zhao J (2014) Multi-label feature selection via information gain. In: International conference on advanced data mining and applications (ADMA). Guilin, China, pp 345–355

    Google Scholar 

  15. Gao C, Zhou J, Xing J, Yue X (2022) parameterized maximum-entropy-based three-way approximate attribute reduction. Int J Approx Reason 151:85–100

    MathSciNet  Google Scholar 

  16. Duan J, Hu Q, Zhang L, Qian Y, Li D (2015) Feature selection for multi-label classification based on neighborhood rough sets. J Comput Res Dev 52(1):56–65

    Google Scholar 

  17. Li J, Yang X, Wang P, Chen X (2018) Stable attribute reduction approach for fuzzy rough set. J Nanjing Univ Sci Technol (Nanjing Li Gong Daxue Xuebao) 42(1):68–75

    Google Scholar 

  18. Reyes O, Morell C, Ventura S (2015) Scalable extensions of the relieff algorithm for weighting and selecting features on the multi-label learning context. Neurocomputing 161:168–182

    Google Scholar 

  19. Dai J, Hu Q, Hu H, Huang D (2017) Neighbor inconsistent pair selection for attribute reduction by rough set approach. IEEE Trans Fuzzy Syst 26(2):937–950

    Google Scholar 

  20. Pawlak Z (1982) Rough set. Int J Comput Inf Sci 11(5):341–356

    Google Scholar 

  21. Zhang C, Li D, Liang J (2018) Hesitant fuzzy linguistic rough set over two universes model and its applications. Int J Mach Learn Cybern 9(4):577–588

    Google Scholar 

  22. Qian Y, Liang J, Pedrycz W, Dang C (2010) Positive approximation: an accelerator for attribute reduction in rough set theory. Artif Intell 174(9–10):597–618

    MathSciNet  Google Scholar 

  23. Yao Y, Zhang X (2017) Class-specific attribute reducts in rough set theory. Inf Sci 418:601–618

    Google Scholar 

  24. Liang J, Wang F, Dang C, Qian Y (2014) A group incremental approach to feature selection applying rough set technique. IEEE Trans Knowl Data Eng 26(2):294–308

    Google Scholar 

  25. Qian Y, Wang Q, Cheng H, Liang J, Dang C (2015) Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst 258:61–78

    MathSciNet  Google Scholar 

  26. Wang C, Wang Y, Shao M, Qian Y, Chen D (2019) Fuzzy rough attribute reduction for categorical data. IEEE Trans Fuzzy Syst 28(5):818–830

    Google Scholar 

  27. Zhao H, Wang P, Hu Q, Zhu P (2019) Fuzzy rough set based feature selection for large-scale hierarchical classification. IEEE Trans Fuzzy Syst 27(10):1891–1903

    Google Scholar 

  28. Ni P, Zhao S, Wang X, Chen H, Li C, Tsang EC (2020) Incremental feature selection based on fuzzy rough sets. Inf Sci 536:185–204

    MathSciNet  Google Scholar 

  29. Hu Q, Yu D, Xie Z, Liu J (2006) Fuzzy probabilistic approximation spaces and their information measures. IEEE Trans Fuzzy Syst 14(2):191–201

    Google Scholar 

  30. Zhang X, Mei C, Chen D, Yang Y, Li J (2019) Active incremental feature selection using a fuzzy-rough-set-based information entropy. IEEE Trans Fuzzy Syst 28(5):901–915

    Google Scholar 

  31. Jensen R, Shen Q (2009) New approaches to fuzzy-rough feature selection. IEEE Trans Fuzzy Syst 4(17):824–838

    Google Scholar 

  32. Jensen R, Shen Q (2007) Fuzzy-rough sets assisted attribute selection. IEEE Trans Fuzzy Syst 15(1):73–89

    Google Scholar 

  33. Zhang L, Hu Q, Duan J, Wang X (2014) Multi-label feature selection with fuzzy rough sets. In: International conference on Rough Sets and Knowledge Technology (RSKT). Shanghai, China, pp 121–128

    Google Scholar 

  34. Lin Y, Hu Q, Liu J, Li J, Wu X (2017) Streaming feature selection for multilabel learning based on fuzzy mutual information. IEEE Trans Fuzzy Syst 25(6):1491–1507

    Google Scholar 

  35. Lin Y, Li Y, Wang C, Chen J (2018) Attribute reduction for multi-label learning with fuzzy rough set. Knowl Based Syst 152:51–61

    Google Scholar 

  36. Li Y, Lin Y, Liu J, Weng W, Shi Z, Wu S (2018) Feature selection for multi-label learning based on kernelized fuzzy rough sets. Neurocomputing 318:271–286

    Google Scholar 

  37. Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17(2–3):191–209

    Google Scholar 

  38. Dai J, Chen J, Liu Y, Hu H (2020) Novel multi-label feature selection via label symmetric uncertainty correlation learning and feature redundancy evaluation. Knowl Based Syst 207:106342

    Google Scholar 

  39. Zhang M-L, Peña JM, Robles V (2009) Feature selection for multi-label naive bayes classification. Inf Sci 179(19):3218–3229

    Google Scholar 

  40. Zhang Y, Zhou Z-H (2008) Multi-label dimensionality reduction via dependence maximization. ACM Trans Knowl Discovery Data 4(3):1–21 (Article No. 14)

    Google Scholar 

  41. Ge L, Li G, You M (2009) Embedded feature selection for multi-label learning. J Nanjing Univ (Nat Sci) 45(5):671–676

    Google Scholar 

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

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Editors for their kindly help and the anonymous referees for their valuable comments and helpful suggestions. The work is partially supported by the National Natural Science Foundation of China (Serial No. 62163016, 62066014), the Natural Science Foundation of Jiangxi Provincial (Serial No. 20212ACB202001, 20232BAB202004), the open project of State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University (Grant No. HJGZ2023203), and the Jiangxi Double Thousand Plan.

Author information

Authors and Affiliations

  1. State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

    Ying Yu

  2. College of Software, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

    Ying Yu, Meiyue Lv, Jin Qian & Jingqin Lv

  3. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China

    Duoqian Miao

Authors
  1. Ying Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meiyue Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingqin Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Duoqian Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Yu.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Y., Lv, M., Qian, J. et al. Fuzzy information gain ratio-based multi-label feature selection with label correlation. Int. J. Mach. Learn. & Cyber. 15, 2737–2747 (2024). https://doi.org/10.1007/s13042-023-02060-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-023-02060-9

Keywords