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Sparse multi-label feature selection via dynamic graph manifold regularization

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Abstract

Multi-label feature selection is a hot topic in multi-label high-dimensional data processing. However, some multi-label feature selection models use manifold graphs. Due to its fixed graph matrix, the model performance is poor, and learning a better fundamental graph matrix is also an urgent problem. Therefore, a sparse multi-label feature selection method is proposed via dynamic graph manifold learning (DMMFS). In this method, the sample space is mapped to the pseudo-label space with a real-label base manifold structure through linear mapping. Then, the Frobenius norm constructed the dynamic graph matrix, and the mutual constraint between the weight matrix and dynamic graph matrix is realized by feature manifold. Finally, experimental comparisons are made on eight multi-label reference data sets with seven of the latest methods. The experimental results prove the superiority of DMMFS.

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References

  1. Gui J, Sun ZN, Jis W et al (2016) Feature selection based on structured sparsity: a comprehensive study. IEEE Trans Neural Netw Learn Syst 28(7):1–18

    MathSciNet  Google Scholar 

  2. Paniri M, Dowlatshahi MB, Nezamabadi-Pour H (2019) MLACO: a multi-label feature selection algorithm based on ant colony optimization. Knowl-Based Syst 192:105285

    Article  Google Scholar 

  3. Kashef S, Nezamabadi-Pour H, Nikpour B (2018) Multi-label feature selection: a comprehensive review and guiding experiments. Wiley Interdiscip Rev Data Min Knowl Discov 8(2):12–40

    Article  Google Scholar 

  4. Ding CC, Zhao M, Lin J et al (2019) Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings. ISA Trans 82:199–215

    Article  Google Scholar 

  5. Labani M, Moradi P, Ahmadizar F et al (2018) A novel multivariate filter method for feature selection in text classification problems. Eng Appl Artif Intell 70:25–37

    Article  Google Scholar 

  6. Yao C, Liu YF, Jiang B et al (2017) LLE score: a new filter-based unsupervised feature selection method based on nonlinear manifold embedding and its application to image recognition. IEEE Trans Image Process 26(11):5257–5269

    Article  MathSciNet  MATH  Google Scholar 

  7. Gonzalez J, Ortega J, Damas M et al (2019) A new multi-objective wrapper method for feature selection-accuracy and stability analysis for BCI. Neurocomputing 333:407–418

    Article  Google Scholar 

  8. Swati J, Hongmei H, Karl J (2018) Information gain directed genetic algorithm wrapper feature selection for credit rating. Appl Soft Comput 69:541–553

    Article  Google Scholar 

  9. Maldonado S, López J (2018) Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for SVM classification. Appl Soft Comput 67:94–105

    Article  Google Scholar 

  10. Kong YC, Yu TW (2018) A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data. Bioinformatics 34(21):3727–3737

    Article  Google Scholar 

  11. Zhang Y, Ma YC (2022) Non-negative multi-label feature selection with dynamic graph constraints. Knowl-Based Syst 238:107924107924

    Article  Google Scholar 

  12. Li XP, Member S, Wang YD et al (2020) A survey on sparse learning models for feature selection. IEEE Trans Cybern 99:1–19

    Google Scholar 

  13. Tang C, Liu XW, Zhu XZ et al (2020) Feature selective projection with low-rank embedding and dual laplacian regularization. IEEE Trans Knowl Data Eng 32(9):1747–1760

    Google Scholar 

  14. Tang C, Zheng X, Liu XW et al (2021) Cross-view locality preserved diversity and consensus learning for multi-view unsupervised feature selection. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2020.3048678

    Article  Google Scholar 

  15. Zhang Y, Ma YC, Yang XF (2022) Multi-label feature selection based on logistic regression and manifold learning. Appl Intell. https://doi.org/10.1007/s10489-021-03008-8

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Lee J, Kim DW (2015) Fast multi-label feature selection based on information-theoretic feature ranking—ScienceDirect. Pattern Recogn 48(9):2761–2771

    Article  MATH  Google Scholar 

  18. Lee J, Kim DW (2017) SCLS: multi-label feature selection based on scalable criterion for large label set. Pattern Recogn 66:342–352

    Article  MathSciNet  Google Scholar 

  19. Gao WF, Hu L, Zhang P (2018) Class-specific mutual information variation for feature selection. Pattern Recogn 2:328–339

    Article  Google Scholar 

  20. Lee J, Kim DW (2018) Scalable multi-label learning based on feature and label dimensionality reduction. Complexity 23:1–15

    MATH  Google Scholar 

  21. Zhang P, Gao WF, Hu JC et al (2020) Multi-label feature selection based on high-order label correlation assumption. Entropy 22(7):797

    Article  MathSciNet  Google Scholar 

  22. Song XY, Li JX, Tang YF et al (2021) JKT: a joint graph convolutional network based deep knowledge tracing. Inf Sci 580:510–523

    Article  MathSciNet  Google Scholar 

  23. Song XY, Li JX, Lei Q et al (2022) Bi-CLKT: bi-graph contrastive learning based knowledge tracing. Knowl-Based Syst 241:108274

    Article  Google Scholar 

  24. Hu XC, Shen YH, Pedrycz W et al (2021) Identification of fuzzy rule-based models with collaborative fuzzy clustering. IEEE Trans Cybern 2:1–14

    Google Scholar 

  25. Liu KY, Yang XB, Fujita H et al (2019) An efficient selector for multi-granularity attribute reduction. Inf Sci 505:457–472

    Article  Google Scholar 

  26. Chen Y, Liu KY, Song JJ et al (2020) Attribute group for attribute reduction. Inf Sci 535:64–80

    Article  MATH  Google Scholar 

  27. Jing YG, Li TR, Fujita H et al (2017) An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view. Inf Sci 411:23–38

    Article  MathSciNet  Google Scholar 

  28. Kawano S (2013) Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions. Stat Anal Data Min 6(6):472–481

    Article  MathSciNet  MATH  Google Scholar 

  29. Kawano S, Misumi T, Konishi S (2012) Semi-supervised logistic discrimination via graph-based regularization. Neural Process Lett 36(3):203–216

    Article  Google Scholar 

  30. Jian L, Li JD, Shu K et al (2016) Multi-label informed feature selection. International Joint Conference on Artificial Intelligence. AAAI Press, 1627–1633

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

    Article  Google Scholar 

  32. Gao WF, Li YH, Hu L (2021) Multi-label feature selection with constrained latent structure shared term. IEEE Trans Neural Netw Learn Syst 2:1–10

    Google Scholar 

  33. Mohapatra P, Chakravarty S, Dash PK (2016) Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system. Swarm Evol Comput 28:144–160

    Article  Google Scholar 

  34. Fan YL, Liu JH, Weng W et al (2021) Multi-label feature selection with constraint regression and adaptive spectral graph. Knowl-Based Syst 212:106621

    Article  Google Scholar 

  35. Tang C, Liu XW, Li MM et al (2018) Robust unsupervised feature selection via dual self-representation and manifold regularization. Knowl-Based Syst 145:109–120

    Article  Google Scholar 

  36. Nie FP, Huang H, Cai X et al (2010) Efficient and robust feature selection via joint \(L_{2,1}\)-norms minimization. International Conference on Neural Information Processing Systems. Curran Associates Inc. 1813–1821

  37. Hashemi A, Dowlatshahi M, Nezamabadi-pour H (2020) Mfs-mcdm: multi-label feature selection using multi-criteria decision making. Knowl-Based Syst 206:106365

    Article  Google Scholar 

  38. Lin Y, Hu Q, Liu J et al (2015) Multi-label feature selection based on maxdependency and min-redundancy. Neurocomputing 168:92–103

    Article  Google Scholar 

  39. Zhang ML, Zhou ZH (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  MATH  Google Scholar 

  40. Mulan. http://mulan.sourceforge.net/datasets.html

  41. Dougherty J, Kohavi R, Sahami M et al (1995) Supervised and unsupervised discretization of continuous features. Mach Learn Proc 2:194–202

    Google Scholar 

  42. Dunn OJ (1961) Multiple comparisons among means. Publ Am Stat Assoc 56(293):52–64

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by the Natural Science Foundation of China (61976130), the Key Research and Development Project of Shaanxi Province (2018KW-021), the Natural Science Foundation of Shaanxi Province (2020JQ-923).

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  1. School of Science, Xi’an Polytechnic University, Xi’an, China

    Yao Zhang & Yingcang Ma

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  1. Yao Zhang
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Correspondence to Yingcang Ma.

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Zhang, Y., Ma, Y. Sparse multi-label feature selection via dynamic graph manifold regularization. Int. J. Mach. Learn. & Cyber. 14, 1021–1036 (2023). https://doi.org/10.1007/s13042-022-01679-4

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