Skip to main content
Springer Nature Link
Log in

Multiple reference points-based multi-objective feature selection for multi-label learning

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In the real world, data often exhibits high-dimensional and complex characteristics. In addition, an object may correspond to multiple class labels. Therefore, filtering and processing such data has become a hot research topic. Multi-label feature selection is one of the key preprocessing techniques for effectively solving redundant information. However, due to the high complexity of multi-label learning feature selection and involving optimization of multiple objectives, careful consideration is required to achieve effective optimization. To address these issues, this paper proposes a multiple reference points-based multi-objective feature selection for multi-label learning. Firstly, the neighborhood crossover strategy utilizes individuals in the same neighborhood for crossover, thereby enhancing the search effect within that decision space. The distribution-based mutation strategy adjusts the mutation probability of different features based on their distribution in selected candidate solutions to increase the likelihood of potentially useful features being selected. Secondly, the multiple reference points-based selection strategy uses a scalar function to uniformly evaluate offspring individuals and control their distance from each other through multiple reference points. This approach helps in selecting convergent solutions with balanced diversity. Finally, experimental results demonstrate that the proposed algorithm attains an optimal performance of 83% across fourteen widely used datasets, outperforming ten state-of-the-art algorithms in terms of effectiveness and performance in multi-label feature selection.

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

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Algorithm 2
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

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

Availability of data and materials

Data sharing not applicable.

Code availability

Code availability not applicable.

References

  1. Zhang ML, Fang JP, Wang YB (2021) Bilabel-specific features for multi-label classification. ACM Transactions on Knowledge Discovery from Data (TKDD) 16(1):1–23

    Google Scholar 

  2. Sun L, Wang L, Ding W, Qian Y, Xu J (2020) Feature selection using fuzzy neighborhood entropy-based uncertainty measures for fuzzy neighborhood multi-granulation rough sets. IEEE Trans Fuzzy Syst 29(1):19–33

    Google Scholar 

  3. Rastogi R, Mortaza S (2021) Multi-label classification with missing labels using label correlation and robust structural learning. Knowl-Based Syst 229:107336

    Google Scholar 

  4. Sha ZC, Liu ZM, Ma C, Chen J (2021) Feature selection for multi-label classification by maximizing full-dimensional conditional mutual information. Appl Intell 51:326–340

    Google Scholar 

  5. Yilmaz OF, Durmusoglu MB (2019) Multi-objective scheduling problem for hybrid manufacturing systems with walking workers. International Journal of Industrial Engineering 26(5)

  6. Zhai T, Wang H, Tang H (2023) Joint optimization of scoring and thresholding models for online multi-label classification. Pattern Recogn 136:109167

    Google Scholar 

  7. Li Y, Hu L, Gao W (2023) Multi-label feature selection via robust flexible sparse regularization. Pattern Recogn 134:109074

    Google Scholar 

  8. Li X, Zhao H, Yu L, Chen H, Deng W, Deng W (2022) Feature extraction using parameterized multisynchrosqueezing transform. IEEE Sens J 22(14):14263–14272

    Google Scholar 

  9. Zhao D, Gao Q, Lu Y, Sun D (2022) Learning view-specific labels and label-feature dependence maximization for multi-view multi-label classification. Appl Soft Comput 124:109071

    Google Scholar 

  10. Jiao R, Xue B, Zhang M (2022) Solving multi-objective feature selection problems in classification via problem reformulation and duplication handling. IEEE Transactions on Evolutionary Computation

  11. Qian W, Li Y, Ye Q, Ding W, Shu W (2023) Disambiguation-based partial label feature selection via feature dependency and label consistency. Information Fusion 94:152–168

    Google Scholar 

  12. Chen K, Xue B, Zhang M, Zhou F (2021) Evolutionary multitasking for feature selection in high-dimensional classification via particle swarm optimization. IEEE Trans Evol Comput 26(3):446–460

    Google Scholar 

  13. Zaman EAK, Mohamed A, Ahmad A (2022) Feature selection for online streaming high-dimensional data: A state-of-the-art review. Applied Soft Computing 109355

  14. Yang J, Zhang M, Liu P, Yu S (2022) Multi-label rhinitis prediction using ensemble neural network chain with pre-training. Appl Soft Comput 122:108839

    Google Scholar 

  15. Wang K, Yang M, Yang W, Wang L (2022) Dual-scale correlation analysis for robust multi-label classification. Appl Intell 52(14):16382–16397

    Google Scholar 

  16. Qian W, Ruan W, Li Y, Huang J (2023) Granular ball-based label enhancement for dimensionality reduction in multi-label data. Applied Intelligence 1–26

  17. 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 

  18. Zhang P, Gao W, Hu J, Li Y (2021) Multi-label feature selection based on the division of label topics. Inf Sci 553:129–153

    MathSciNet  Google Scholar 

  19. Hashemi A, Dowlatshahi MB, Nezamabadi-pour H (2021) An efficient pareto-based feature selection algorithm for multi-label classification. Inf Sci 581:428–447

    MathSciNet  Google Scholar 

  20. Hu J, Li Y, Gao W, Zhang P (2020) Robust multi-label feature selection with dual-graph regularization. Knowl-Based Syst 203:106126

    Google Scholar 

  21. Xue Y, Zhu H, Liang J, SŁowik A (2021) Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification. Knowl-Based Syst 227:107218

  22. Li G, Li Y, Zheng Y, Li Y, Hong Y, Zhou X (2021) A novel feature selection approach with pareto optimality for multi-label data. Applied Intelligence 1–18

  23. Liu W, Yuan J, Lyu G, Feng S (2023) Label driven latent subspace learning for multi-view multi-label classification. Appl Intell 53(4):3850–3863

    Google Scholar 

  24. Zhang J, Wu H, Jiang M, Liu J, Li S, Tang Y, Long J (2023) Group-preserving label-specific feature selection for multi-label learning. Expert Syst Appl 213:118861

    Google Scholar 

  25. Huang R, Wu Z (2021) Multi-label feature selection via manifold regularization and dependence maximization. Pattern Recogn 120:108149

    Google Scholar 

  26. Ma J, Chiu BCY, Chow TW (2020) Multilabel classification with group-based mapping: a framework with local feature selection and local label correlation. IEEE Transactions on Cybernetics 52(6):4596–4610

    Google Scholar 

  27. Liu J, Lin Y, Ding W, Zhang H, Du J (2022) Fuzzy mutual information-based multilabel feature selection with label dependency and streaming labels. IEEE Trans Fuzzy Syst 31(1):77–91

  28. Sun L, Yin T, Ding W, Qian Y, Xu J (2021) Feature selection with missing labels using multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy. IEEE Trans Fuzzy Syst 30(5):1197–1211

    Google Scholar 

  29. Li Y, Hu L, Gao W (2023) Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation. Pattern Recogn 134:109120

    Google Scholar 

  30. Gao W, Hao P, Wu Y, Zhang P (2023) A unified low-order information-theoretic feature selection framework for multi-label learning. Pattern Recogn 134:109111

    Google Scholar 

  31. Hancer E, Xue B, Zhang M (2023) An evolutionary filter approach to feature selection in classification for both single-and multi-objective scenarios. Knowl-Based Syst 280:111008

    Google Scholar 

  32. Yuan G, Zhai Y, Tang J, Zhou X (2023) Cscim fs: Cosine similarity coefficient and information measurement criterion-based feature selection method for high-dimensional data. Neurocomputing 552:126564

    Google Scholar 

  33. Yılmaz ÖF et al (2022) Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches. Ann Oper Res 319(2):1793–1843

    Google Scholar 

  34. Yılmaz ÖF (2022) An integrated bi-objective u-shaped assembly line balancing and parts feeding problem: optimization model and exact solution method. Ann Math Artif Intell 90(7–9):679–696

    MathSciNet  Google Scholar 

  35. Bidgoli AA, Ebrahimpour-Komleh H, Rahnamayan S (2019) A novel multi-objective binary differential evolution algorithm for multi-label feature selection. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1588–1595. IEEE

  36. Paul D, Kumar R, Saha S, Mathew J (2021) Multi-objective cuckoo search-based streaming feature selection for multi-label dataset. ACM Transactions on Knowledge Discovery from Data (TKDD) 15(6):1–24

    Google Scholar 

  37. Dong H, Sun J, Sun X, Ding R (2020) A many-objective feature selection for multi-label classification. Knowl-Based Syst 208:106456

    Google Scholar 

  38. Paniri M, Dowlatshahi MB, Nezamabadi-Pour H (2020) Mlaco: A multi-label feature selection algorithm based on ant colony optimization. Knowl-Based Syst 192:105285

    Google Scholar 

  39. Bidgoli AA, Ebrahimpour-Komleh H, Rahnamayan S (2021) Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection. Inf Sci 547:1–17

    MathSciNet  Google Scholar 

  40. Qian C (2023) Can evolutionary clustering have theoretical guarantees? IEEE Transactions on Evolutionary Computation

  41. Li W, Chai Z, Tang Z (2021) A decomposition-based multi-objective immune algorithm for feature selection in learning to rank. Knowl-Based Syst 234:107577

    Google Scholar 

  42. Nguyen BH, Xue B, Andreae P, Ishibuchi H, Zhang M (2019) Multiple reference points-based decomposition for multiobjective feature selection in classification: Static and dynamic mechanisms. IEEE Trans Evol Comput 24(1):170–184

    Google Scholar 

  43. Nouri-Moghaddam B, Ghazanfari M, Fathian M (2021) A novel multi-objective forest optimization algorithm for wrapper feature selection. Expert Syst Appl 175:114737

    Google Scholar 

  44. Liu Y, Chen H, Li T, Li W (2023) A robust graph based multi-label feature selection considering feature-label dependency. Appl Intell 53(1):837–863

    Google Scholar 

  45. Qian W, Huang J, Xu F, Shu W, Ding W (2023) A survey on multi-label feature selection from perspectives of label fusion. Information Fusion 100:101948

    Google Scholar 

  46. Hashemi A, Dowlatshahi MB, Nezamabadi-Pour H (2020) Mfs-mcdm: Multi-label feature selection using multi-criteria decision making. Knowl-Based Syst 206:106365

  47. Zhang P, Liu G, Song J (2023) Mfsjmi: Multi-label feature selection considering join mutual information and interaction weight. Pattern Recogn 138:109378

    Google Scholar 

  48. Paniri M, Dowlatshahi MB, Nezamabadi-pour H (2021) Ant-td: Ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection. Swarm Evol Comput 64:100892

    Google Scholar 

  49. Tsoumakas G, Spyromitros-Xioufis E, Vilcek J, Vlahavas I (2011) Mulan: A java library for multi-label learning. The Journal of Machine Learning Research 12:2411–2414

    MathSciNet  Google Scholar 

  50. Zhang ML, Zhou ZH (2007) Ml-knn: A lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Google Scholar 

  51. Tarekegn AN, Giacobini M, Michalak K (2021) A review of methods for imbalanced multi-label classification. Pattern Recogn 118:107965

  52. Carrasco J, García S, Rueda M, Das S, Herrera F (2020) Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review. Swarm Evol Comput 54:100665

    Google Scholar 

  53. Ahmadianfar I, Heidari AA, Noshadian S, Chen H, Gandomi AH (2022) Info: An efficient optimization algorithm based on weighted mean of vectors. Expert Syst Appl 195:116516

    Google Scholar 

Download references

Funding

This work is supported by the National Natural Science Foundation of China (No.62366019 and No.61966016), and the Natural Science Foundation of Jiangxi Province, China (No.20224BAB202020).

Author information

Authors and Affiliations

  1. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Yangtao Chen

  2. School of Software, Jiangxi Agricultural University, Nanchang, 330045, China

    Wenbin Qian

Authors
  1. Yangtao Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wenbin Qian
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Yangtao Chen: Software, Writing - Original draft. Wenbin Qian: Methodology, Conceptualization, Writing-review and editing.

Corresponding author

Correspondence to Wenbin Qian.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

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

Chen, Y., Qian, W. Multiple reference points-based multi-objective feature selection for multi-label learning. Appl Intell 54, 4952–4978 (2024). https://doi.org/10.1007/s10489-024-05387-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-024-05387-0

Keywords