Skip to main content
SpringerLink
Log in

LEFSA: label enhancement-based feature selection with adaptive neighborhood via ant colony optimization for multilabel learning

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

Abstract

To date, multilabel learning has garnered attention increased from scholars and has a significant effect on practical applications; however, most feature selection models with classification margin cannot fully reflect the correlations between the feature and label sets. This work constructs a label enhancement-based feature selection method via ant colony optimization (ACO) on multilabel data. First, by combining the feature cosine distance and label distance of the samples, a global distance between the samples is presented, and an adjustment parameter is defined to dynamically regulate the label distance between the samples. The discriminant relation between the samples is presented to distinguish the homogeneous or heterogeneous samples of the target sample. An average classification margin-based adaptive neighborhood radius of the target sample is designed. Thus, a new adaptive fuzzy neighborhood rough set is proposed. Second, by integrating the algebraic and information viewpoints, the roughness degree is fused with the multilabel fuzzy neighborhood mutual information. The weight of each label is generated based on the label distribution of all the samples. Label enhancement-based fuzzy neighborhood mutual information can be determined to generate the final correlation of each feature and label set. Finally, Pearson correlation coefficient with an upper approximation will be applied to construct the pheromone initialization of the feature. Two metrics can be used as the heuristic information of the ACO to guide the ants to select significant features. Thus, a label enhancement-based multilabel feature subset selection methodology will be provided to obtain a superior set of features. The results from experiments confirm the capability of the proposed methodology in implementing significant classification effects on 13 datasets.

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

Data availability

The datasets that support the findings of the research are available from the corresponding author upon reasonable request.

References

  1. Ma JH, Chow TWS, Zhang HJ (2022) Semantic-gap-oriented feature selection and classifier construction in multilabel learning. IEEE Trans Cybernet 52(1):101–115

    Google Scholar 

  2. Hashemi A, Dowlatshahi MB, Nezamabadi-pour H (2020) MFS-MCDM: multilabel feature selection using multi-criteria decision making. Knowl-Based Syst 206:106365

    Google Scholar 

  3. Sun L, Si SS, Ding WP, Wang XY, Xu JC (2023) TFSFB: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data. Inform Fusion 95:91–108

    Google Scholar 

  4. Lin YJ, Hu QH, Liu JH, Zhu XQ, Wu XD (2022) MULFE: multilabel learning via label-specific feature space ensemble. ACM Trans Knowl Discovery Data 16(1): 5:1–5:24.

  5. Hashemi A, Dowlatshahi MB, Nezamabadi-pour H (2020) MGFS: A multilabel graph-based feature selection algorithm via PageRank centrality. Expert Syst Appl 142:113024

    Google Scholar 

  6. Yao EL, Li DY, Zhai YH, Zhang C (2022) Multilabel feature selection based on relative discernibility pair matrix. IEEE Trans Fuzzy Syst 30(7):2388–2401

    Google Scholar 

  7. Ma JH, Zhang HJ, Chow TWS (2021) Multilabel classification with label-specific features and classifiers: A coarse- and fine-tuned framework. IEEE Trans Cybernet 51(2):1028–1042

    Google Scholar 

  8. Bayati H, Dowlatshahi MB, Hashemi A (2022) MSSL: a memetic-based sparse subspace learning algorithm for multi-label classification. Int J Mach Learn Cybern 13:3607–3624

    Google Scholar 

  9. Li YH, Hu L, Gao WF (2023) Multilabel feature selection via robust flexible sparse regularization. Pattern Recogn 134:109074

    Google Scholar 

  10. Sun L, Wang LY, Ding WP, Qian YH, Xu JC (2020) Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems. Knowl-Based Syst 192:105373

    Google Scholar 

  11. Xie JJ, Hu BQ, Jiang HB (2022) A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets. Int J Approx Reasoning 144:1–17

    MathSciNet  Google Scholar 

  12. Liu JH, Lin YJ, Du JX, Zhang HB, Chen ZY, Zhang J (2022) ASFS: A novel streaming feature selection for multilabel data based on neighborhood rough set. Appl Intell. https://doi.org/10.1007/s10489-022-03366-x

    Article  Google Scholar 

  13. Wu YL, Liu JH, Yu XH, Lin YJ, Li SZ Neighborhood rough set based multilabel feature selection with label correlation, Concurrency and Computation: Practice & Experience (2022), https://doi.org/10.1002/cpe.7162

  14. Sun L, Zhang JX, Ding WP, Xu JC (2022) Mixed measure-based feature selection using the Fisher score and neighborhood rough sets. Appl Intell 52:17264–17288

    Google Scholar 

  15. Wang CZ, Shao MW, He Q, Qian YH, Qi YL (2016) Feature subset selection based on fuzzy neighborhood rough sets. Knowl-Based Syst 111:173–179

    Google Scholar 

  16. Sun L, Wang LY, Ding WP, Qian YH, Xu JC (2021) Feature selection using fuzzy neighborhood entropy-based uncertainty measures for fuzzy neighborhood Multigranulation rough sets. IEEE Trans Fuzzy Syst 29(1):19–33

    Google Scholar 

  17. Sun L, Li MM, Ding WP, Zhang E (2022) AFNFS: Adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced data. Inf Sci 612:724–744

    Google Scholar 

  18. Xu JC, Shen KL, Sun L (2022) Multilabel feature selection based on fuzzy neighborhood rough sets. Complex Intell Syst 8:2105–2129

    Google Scholar 

  19. Chen PP, Lin ML, Liu JH (2020) Multilabel attribute reduction based on variable precision fuzzy neighborhood rough set. IEEE Access 8:133565–133576

    Google Scholar 

  20. Shu WH, Qian WB, Xie YH (2022) Incremental neighborhood entropy-based feature selection for mixed-type data under the variation of feature set. Appl Intell 52:4792–4806

    Google Scholar 

  21. Sun L, Zhang JX, Ding WP, Xu JC (2022) Feature reduction for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors. Inf Sci 593:591–613

    Google Scholar 

  22. Lin YJ, Hu QH, Liu JH, Chen JK, Duan J (2016) Multilabel feature selection based on neighborhood mutual information. Appl Soft Comput 38:244–256

    Google Scholar 

  23. Huang MM, Sun L, Xu JC, Zhang SG (2020) Multilabel feature selection using relief and minimum redundancy maximum relevance based on neighborhood rough sets. IEEE Access 8:62011–62031

    Google Scholar 

  24. Wang CX, Lin YJ, Liu JH (2019) Feature selection for multilabel learning with missing labels. Appl Intell 49:3027–3042

    Google Scholar 

  25. Sun L, Yin TY, Ding WP, Qian YH, Xu JC (2020) Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems. Inf Sci 537:401–424

    MathSciNet  Google Scholar 

  26. Sun L, Yin TY, Ding WP, Qian YH, Xu JC (2022) 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 

  27. Xu N, Liu YP, Geng X (2021) Label enhancement for label distribution learning. IEEE Trans Knowl Data Eng 33(4):1632–1643

    Google Scholar 

  28. Lin YJ, Liu HY, Zhao H, Hu QH, Zhu XQ, Wu XD (2023) Hierarchical feature selection based on label distribution learning. IEEE Trans Knowl Data Eng 35(6):5964–5976

    Google Scholar 

  29. Qian WB, Xiong CZ, Qian YH, Wang YL (2022) Label enhancement-based feature selection via fuzzy neighborhood discrimination index. Knowl-Based Syst 250:109119

    Google Scholar 

  30. Long XD, Qian WB, Wang YL, Shu WH (2021) Cost-sensitive feature selection on multilabel data via neighborhood granularity and label enhancement. Appl Intell 51:2210–2232

    Google Scholar 

  31. Bayati H, Dowlatshahi MB, Paniri M MLPSO: a filter multilabel feature selection based on particle swarm optimization, In: 2020 25th International Computer Conference, Computer Society of Iran (CSICC) (2020)https://doi.org/10.1109/CSICC49403.2020.9050087.

  32. Sun L, Chen SS, Xu JC, Tian Y (2019) Improved monarch butterfly optimization algorithm based on opposition-based learning and random local perturbation. Complexity 2019:4182148

    Google Scholar 

  33. Ma WP, Zhou XB, Zhu H, Li LW, Jiao LC (2021) A two-stage hybrid ant colony optimization for high-dimensional feature selection. Pattern Recogn 116:107933

    Google Scholar 

  34. Hashemi A, Joodaki M, Joodaki NZ, Dowlatshahi MB (2022) Ant colony optimization equipped with an ensemble of heuristics through multi-criteria decision making: a case study in ensemble feature selection. Appl Soft Comput 14:109046

    Google Scholar 

  35. Yang ZY, Ye J, Ao JX, Ji YX (2021) Feature selection method based on ant colony optimization algorithm and improved neighborhood discernibility matrix. Bio-Inspired Comput 1565:116–131

    Google Scholar 

  36. Paniri M, Dowlatshahi MB, Nezamabadi-pour H (2020) MLACO: A multilabel feature selection algorithm based on ant colony optimization. Knowl-Based Syst 192:105285

    Google Scholar 

  37. Hashemi A, Dowlatshahi MB, Nezamabadi-pour H (2021) VMFS: A VIKOR-based multi-target feature selection. Expert Syst Appl 182:115224

    Google Scholar 

  38. Dorigo M, Gambardella LM (1997) Ant colonies for the travelling salesman problem. Biosystems 43(2):73–81

    Google Scholar 

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

    Google Scholar 

  40. Qian WB, Long XD, Wang YL, Xie YH (2020) Multilabel feature selection based on label distribution and feature complementarity. Appl Soft Comput 90:106167

    Google Scholar 

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

    Google Scholar 

  42. Duan J, Hu QH, Zhang LJ, Qian YH, Li DY (2015) Feature selection for multilabel classification based on neighborhood rough sets. Chin Comput Res Dev 52(1):56–65

    Google Scholar 

  43. Sun L, Wang TX, Ding WP, Xu JC, Lin YJ (2021) Feature selection using Fisher score and multilabel neighborhood rough sets for multilabel classification. Inf Sci 578:887–912

    MathSciNet  Google Scholar 

  44. Lin YJ, Li YW, Wang CX, Chen JK (2018) Attribute reduction for multilabel learning with fuzzy rough set. Knowl-Based Syst 152:51–61

    Google Scholar 

  45. Sun L, Wang TX, Ding WP, Xu JC, Tan AH (2022) Two-stage-neighborhood-based multilabel classification for incomplete data with missing labels. Int J Intell Syst 37:6773–6810

    Google Scholar 

  46. Sun L, Wang XY, Ding WP, Xu JC (2022) TSFNFR: Two-stage fuzzy neighborhood-based feature reduction with binary whale optimization algorithm for imbalanced data. Knowl-Based Syst 256:109849

    Google Scholar 

  47. Lee J, Kim DW (2013) Feature selection for multilabel classification using multivariate mutual information. Pattern Recogn Lett 34(3):349–357

    Google Scholar 

  48. Lin YJ, Hu QH, Liu JH, Li JJ, Wu XD (2017) Streaming feature selection for multilabel learning based on fuzzy mutual information. IEEE Trans Fuzzy Syst 25(6):1491–1507

    Google Scholar 

  49. Huang R, Jiang WD, Sun GL (2018) Manifold-based constraint Laplacian score for multilabel feature selection. Pattern Recogn Lett 112:346–352

    Google Scholar 

  50. Sun L, Chen YS, Xu JC (2022) Multilabel feature selection algorithm based on improved ReliefF. Chin J Shandong Univ (Nat Sci) 57(4):1–11

    Google Scholar 

  51. Hu JC, Li YH, Xu GC, Gao WF (2022) Dynamic subspace dual-graph regularized multilabel feature selection. Neurocomputing 467:184–196

    Google Scholar 

  52. Tan AH, Liang JY, Wu WZ, Zhang J, Sun L, Chen C (2021) Fuzzy rough discrimination and label weighting for multilabel feature selection. Neurocomputing 465:128–140

    Google Scholar 

  53. Zhang ML, Zhou ZH (2007) ML-KNN: A lazy learning approach to multilabel learning. Pattern Recogn 40:2038–2048

    Google Scholar 

  54. Zhang QW, Zhong Y, Zhang ML Feature-induced labeling information enrichment for multilabel learning, In: 32nd AAAI conference on artificial intelligence (2017) 4446–4453.

  55. Cheng YS, Li QY, Wang YB, Zheng WJ (2022) Multi-view multilabel learning with view feature attention allocation. Neurocomputing 501:857–874

    Google Scholar 

  56. Guo BL, Tao H, Hou CP, Yi DY (2020) Semi-supervised multilabel feature learning via label enlarged discriminant analysis. Knowl Inf Syst 62:2383–2417

    Google Scholar 

  57. Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86–92

    MathSciNet  Google Scholar 

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

    MathSciNet  Google Scholar 

  59. Sun L, Wang TX, Ding WP, Xu JC (2023) Partial multilabel learning using fuzzy neighbourhood-based ball clustering and kernel extreme learning machine. IEEE Trans Fuzzy Syst. 31(7): 2277–2291

    Google Scholar 

  60. Tan AH, Ji XW, Liang JY, Tao YZ, Wu WZ, Pedrycz W (2022) Weak multilabel learning with missing labels via instance granular discrimination. Inf Sci 594:200–216

    Google Scholar 

Download references

Acknowledgements

This research was funded by the National Natural Science Foundation of China under Grants 62076089, 61772176, 61976082, and 61976120; and the Natural Science Key Foundation of Jiangsu Education Department under Grant 21KJA510004.

Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China

    Lin Sun, Yusheng Chen & Jiucheng Xu

  2. School of Information Science and Technology, Nantong University, Nantong, 226019, China

    Weiping Ding

  3. College of Artifcial Intelligence, Tianjin University of Science and Technology, Tianjin, 300457, China

    Lin Sun

Authors
  1. Lin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yusheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weiping Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiucheng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LS: Funding acquisition, project administration, methodology, writing—original draft, validation. YC: investigation, conceptualization, software, validation, writing. WD: writing—review & editing. JX: writing—review & editing.

Corresponding authors

Correspondence to Lin Sun or Weiping Ding.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this work.

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

Sun, L., Chen, Y., Ding, W. et al. LEFSA: label enhancement-based feature selection with adaptive neighborhood via ant colony optimization for multilabel learning. Int. J. Mach. Learn. & Cyber. 15, 533–558 (2024). https://doi.org/10.1007/s13042-023-01924-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-023-01924-4

Keywords

Search

Navigation