Skip to main content
SpringerLink
Log in

Feature selection using relative dependency complement mutual information in fitting fuzzy rough set model

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

As a reliable and valid tool for analyzing uncertain information, fuzzy rough set theory has attracted widespread concern in feature selection. However, the performance of fuzzy rough set model is generally affected by various factors, for instance, large data distribution differences, unreasonable settings for fuzzy information granules and feature evaluation functions with single perspective. Considering these problems, a fitting fuzzy rough set model with relative dependency complement mutual information is proposed in this paper. First, the relative distance is introduced to eliminate the influence of data distribution on the fuzzy similarity relation. Then, by analyzing the similarity distributions of samples with regard to decisions, a fitting fuzzy neighborhood radius is proposed to improve the fuzzy information granules, and a fitting fuzzy rough set model is proposed based on the relative distance and the fitting fuzzy neighborhood radius. Moreover, considering the complementary characteristics between fuzzy information granularity and fuzzy information entropy, the related definitions of relative complement information entropy in the fitting fuzzy rough set model are offered, and a multiview uncertainty measure based on relative dependency complement mutual information is constructed to comprehensively analyze the uncertainty of information. Finally, a heuristic feature selection algorithm is designed. A series of experiments designed in this paper prove the superiority of the 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
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Li J, Liu H (2017) Challenges of feature selection for big data analytics. IEEE Intell Syst 32 (2):9–15. https://doi.org/10.1109/MIS.2017.38

    Article  MathSciNet  Google Scholar 

  2. El-Hasnony IM, Barakat SI, Elhoseny M, Mostafa RR (2020) Improved feature selection model for big data analytics. IEEE Access 8:66989–67004. https://doi.org/10.1109/ACCESS.2020.2986232

    Article  Google Scholar 

  3. An S, Hu Q, Wang C, Wang C, Guo G, Li P (2022) Data reduction based on NN-kNN measure for NN classification and regression. Int J Mach Learn Cybern 13(3):765–781. https://doi.org/10.1007/s13042-021-01327-3

    Article  Google Scholar 

  4. Lin Y, Hu Q, Liu J, et al. (2022) MULFE: multi-label learning via label-specific feature space ensemble. ACM Transactions on Knowledge Discovery from Data (TKDD) 16(1):5:1–5:24. https://doi.org/10.1145/3451392

    Article  Google Scholar 

  5. 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. https://doi.org/10.1109/TFUZZ.2019.2949765

    Article  Google Scholar 

  6. Lin Y, Liu H, Zhao H, et al (2022) Hierarchical feature selection based on label distribution learning. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2022.3177246

  7. Hamidzadeh J (2021) Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator. Soft Comput 25(4):2911–2933. https://doi.org/10.1007/s00500-020-05349-x

    Article  Google Scholar 

  8. Ali G, Afzal M, Asif M et al (2022) Attribute reduction approaches under interval-valued q-rung orthopair fuzzy soft framework. App Intell 52(8):8975–9000

    Article  Google Scholar 

  9. Kashani SMZ, Hamidzadeh J (2020) Feature selection by using privacy-preserving of recommendation systems based on collaborative filtering and mutual trust in social networks. Soft Comput 24(15):11425–11440. https://doi.org/10.1007/s00500-019-04605-z

    Article  Google Scholar 

  10. Skowron A, Dutta S (2018) Rough sets: past, present, and future. Nat Comput 17(4):855–876. https://doi.org/10.1007/s11047-018-9700-3

    Article  MathSciNet  Google Scholar 

  11. Jayasuruthi L, Shalini A, Kumar VV (2018) Application of rough set theory in data mining market analysis using rough sets data explorer. J Comput Theor Nanosci 15(6–7):2126–2130

    Article  Google Scholar 

  12. Hu Q, Yu D, Liu J et al (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594. https://doi.org/10.1016/j.ins2008.05.024

    Article  MathSciNet  MATH  Google Scholar 

  13. Chen D, Zhang L, Zhao S et al (2011) A novel algorithm for finding reducts with fuzzy rough sets. IEEE Trans Fuzzy Syst 20(2):385–389. https://doi.org/10.1109/TFUZZ.2011.2173695

    Article  Google Scholar 

  14. Sun L, Wang L, Qian Y et al (2019) Feature selection using Lebesgue and entropy measures for incomplete neighborhood decision systems. Knowl-Based Syst 186:104942. https://doi.org/10.1016/j.knosys.2019.104942

    Article  Google Scholar 

  15. Xu J, Qu K, Yuan M et al (2021) Feature selection combining information theory view and algebraic view in the neighborhood decision system. Entropy 23(6):704. https://doi.org/10.3390/e23060704

    Article  MathSciNet  Google Scholar 

  16. Yang X, Chen H, Li T et al (2021) Neighborhood rough sets with distance metric learning for feature selection. Knowl-Based Syst 224:107076. https://doi.org/10.1016/j.knosys.2021.107076

    Article  Google Scholar 

  17. Ji W, Pang Y, Jia X et al (2021) Fuzzy rough sets and fuzzy rough neural networks for feature selection: a review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11(3):e1402. https://doi.org/10.1002/widm.1402

    Google Scholar 

  18. Akram M, Ali G, Alcantud JCR (2022) Attributes reduction algorithms for m-polar fuzzy relation decision systems. Int J Approx Reason 140:232–254. https://doi.org/10.1016/j.ijar.2021.10.005

    Article  MathSciNet  MATH  Google Scholar 

  19. Zhang X, Mei C, Chen D et al (2018) A fuzzy rough set-based feature selection method using representative instances. Knowl-Based Syst 151:216–229. https://doi.org/10.1016/j.knosys.2018.03.031

    Article  Google Scholar 

  20. Sheeja TK, Kuriakose AS (2018) A novel feature selection method using fuzzy rough sets. Comput Ind 97:111–116. https://doi.org/10.1016/j.compind.2018.01.014

    Article  Google Scholar 

  21. Atef M, Atik E, El Fattah A (2021) Some extensions of covering-based multigranulation fuzzy rough sets from new perspectives. Soft Comput 25(8):6633–6651. https://doi.org/10.1007/s00500-021-05659-8

    Article  MATH  Google Scholar 

  22. Sun L, Xu JC, Wang W et al (2016) Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification. Genet Mol Res 15(3):15038990

    Article  Google Scholar 

  23. Ali G, Akram M, Alcantud JCR (2020) Attributes reductions of bipolar fuzzy relation decision systems. Neural Comput Applic 32(14):10051–10071. https://doi.org/10.1007/s00521-019-04536-8

    Article  Google Scholar 

  24. Xu J, Sun Y, Qu K, et al. (2022) Online group streaming feature selection using entropy-based uncertainty measures for fuzzy neighborhood rough sets. Complex & Intelligent Systems, pp 1–20. https://doi.org/10.1007/s40747-022-00763-0

  25. Tiwari AK, Shreevastava S, Som T et al (2018) Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction. Expert Syst Appl 101:205–212. https://doi.org/10.1016/j.eswa.2018.02.009

    Article  Google Scholar 

  26. Ducange P, Fazzolari M, Marcelloni F (2020) An overview of recent distributed algorithms for learning fuzzy models in big data classification. Journal of Big Data 7(1):1–29. https://doi.org/10.1186/s40537-020-00298-6

    Article  Google Scholar 

  27. Hamidzadeh J, Rezaeenik E, Moradi M (2021) Predicting users’ preferences by fuzzy rough set quarter-sphere support vector machine. Appl Soft Comput 112:107740. https://doi.org/10.1016/j.asoc.2021.107740

    Article  Google Scholar 

  28. Dai J, Chen J (2020) Feature selection via normative fuzzy information weight with application into tumor classification. Appl Soft Comput 92:106299. https://doi.org/10.1016/j.asoc.2020.106299

    Article  Google Scholar 

  29. Akram M, Ali G, Alcantud JCR, et (2021) Parameter reductions in N-soft sets and their applications in decision-making. Expert Syst 38(1):e12601. https://doi.org/10.1111/exsy.12601

    Article  Google Scholar 

  30. Zhu X, Wu X (2004) Class noise vs. attribute noise: a quantitative study. Artif Intell Rev 22(3):177–210. https://doi.org/10.1007/s10462-004-0751-8

    Article  MATH  Google Scholar 

  31. Moslemnejad S, Hamidzadeh J (2021) Weighted support vector machine using fuzzy rough set theory. Soft Comput 25(31):8461–8481. https://doi.org/10.1007/s00500-021-05773-7

    Article  Google Scholar 

  32. Hu Q, Yu D, Pedrycz W et al (2010) Kernelized fuzzy rough sets and their applications. IEEE Trans Knowl Data Eng 23(11):1649–1667. https://doi.org/10.1109/TKDE.2010.260

    Article  Google Scholar 

  33. Hu Q, Zhang L, An S et al (2011) On robust fuzzy rough set models. IEEE Trans Fuzzy Syst 20(4):636–651. https://doi.org/10.1109/TFUZZ.2011.2181180

    Article  Google Scholar 

  34. Wang C, Huang Y, Ding W et al (2021) Attribute reduction with fuzzy rough self-information measures. Inform Sci 549:68–86. https://doi.org/10.1016/j.ins.2020.11.021

    Article  MathSciNet  MATH  Google Scholar 

  35. An S, Hu Q, Wang C (2021) Probability granular distance-based fuzzy rough set model. Appl Soft Comput 102:107064. https://doi.org/10.1016/j.asoc.2020.107064

    Article  Google Scholar 

  36. Zhang C, Li D, Liang J (2020) Multi-granularity three-way decisions with adjustable hesitant fuzzy linguistic multigranulation decision-theoretic rough sets over two universes. Inform Sci 507:665–683. https://doi.org/10.1016/j.ins.2019.01.033

    Article  MathSciNet  MATH  Google Scholar 

  37. Wang C, Shao M, He Q et al (2016) Feature subset selection based on fuzzy neighborhood rough sets. Knowl-Based Syst 111:173–179. https://doi.org/10.1016/j.knosys.2016.08.009

    Article  Google Scholar 

  38. Pal SK (2020) Granular mining and big data analytics: rough models and challenges. Proceedings of the National Academy of Sciences. India Section A: Physical Sciences 90(2):193–208. https://doi.org/10.1007/s40010-018-0578-3

    MathSciNet  Google Scholar 

  39. Xia S, Zhang Z, Li W et al (2020) GBNRS: a novel rough set algorithm for fast adaptive attribute reduction in classification. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2020.2997039

  40. Wang C, Qi Y, Shao M et al (2016) A fitting model for feature selection with fuzzy rough sets. IEEE Trans Fuzzy Syst 25(4):741–753. https://doi.org/10.1109/TFUZZ.2016.2574918

    Article  Google Scholar 

  41. Tan A, Wu WZ, Qian Y et al (2018) Intuitionistic fuzzy rough set-based granular structures and attribute subset selection. IEEE Trans Fuzzy Syst 27(3):527–539. https://doi.org/10.1109/TFUZZ.2018.2862870

    Article  Google Scholar 

  42. Xu J, Wang Y, Mu H et al (2019) Feature genes selection based on fuzzy neighborhood conditional entropy. Journal of Intelligent & Fuzzy Systems 36(1):117–126. https://doi.org/10.3233/JIFS-18100

    Article  Google Scholar 

  43. Yuan Z, Chen H, Xie P et al (2021) Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions. Appl Soft Comput 107:107353. https://doi.org/10.1016/j.asoc.2021.107353

    Article  Google Scholar 

  44. An S, Zhao E, Wang C et al (2021) Relative fuzzy rough approximations for feature selection and classification. IEEE Transactions on Cybernetics

  45. Sun L, Wang L, Ding W et al (2020) Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems. Knowl-Based Syst 192:105373. https://doi.org/10.1016/j.knosys.2019.105373

    Article  Google Scholar 

  46. Ni P, Zhao S, Wang X et al (2019) PARA: A positive-region based attribute reduction accelerator. Inform Sci 503:533–550. https://doi.org/10.1016/j.ins.2019.07.038

    Article  Google Scholar 

  47. Jensen R, Shen Q (2008) New approaches to fuzzy-rough feature selection. IEEE Trans Fuzzy Syst 17(4):824–838. https://doi.org/10.1109/TFUZZ.2008.924209

    Article  Google Scholar 

  48. Hu Q, Xie Z, Yu D (2008) Comments on fuzzy probabilistic approximation spaces and their information measures. IEEE Trans Fuzzy Syst 16(2):549–551. https://doi.org/10.1109/TFUZZ.2007.896321

    Article  Google Scholar 

  49. Hu Q, Zhang L, Zhang D et al (2011) Measuring relevance between discrete and continuous features based on neighborhood mutual information. Expert Syst Appl 38(9):10737–10750. https://doi.org/10.1016/j.eswa.2011.01.023

    Article  Google Scholar 

  50. Lu H, Chen J, Yan K, et (2017) A hybrid feature selection algorithm for gene expression data classification. Neurocomputing 256:56–62. https://doi.org/10.1016/j.neucom.2016.07.080

    Article  Google Scholar 

  51. Wang G (2003) Rough reduction in algebra view and information view. Int J Intell Syst 18(6):679–688. https://doi.org/10.1002/int.10109

    Article  MATH  Google Scholar 

  52. Sun L, Wang L, Ding W et al (2020) Feature selection using fuzzy neighborhood entropy-based uncertainty measures for fuzzy neighborhood multigranulation rough sets. IEEE Trans Fuzzy Syst 29(1):19–33. https://doi.org/10.1109/TFUZZ.2020.2989098

    Article  Google Scholar 

  53. Xu J, Yuan M, Ma Y (2022) Feature selection using self-information and entropy-based uncertainty measure for fuzzy neighborhood rough set. Complex & Intelligent Systems 8(1):287–305. https://doi.org/10.1007/s40747-021-00356-3

    Article  Google Scholar 

  54. Xu J, Qu K, Meng X et al (2022) Feature selection based on multiview entropy measures in multiperspective rough set. International Journal of Intelligent Systems. https://doi.org/10.1002/int.22878

  55. Sun L, Li M, Ding W et al (2022) AFNFS: adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced data. Inform Sci 612:724–744. https://doi.org/10.1016/j.ins.2022.08.118

    Article  Google Scholar 

  56. Liang J, Chin KS, Dang C et al (2002) A new method for measuring uncertainty and fuzziness in rough set theory. Int J Gen Syst 31(4):331–342. https://doi.org/10.1080/0308107021000013635

    Article  MathSciNet  MATH  Google Scholar 

  57. Qian Y, Liang J, Wei-zhi ZW et al (2010) Information granularity in fuzzy binary GrC model. IEEE Trans Fuzzy Syst 19(2):253–264. https://doi.org/10.1109/TFUZZ.2010.2095461

    Article  Google Scholar 

  58. Zhao J, Zhang Z, Han C et al (2015) Complement information entropy for uncertainty measure in fuzzy rough set and its applications. Soft Comput 19(7):1997–2010. https://doi.org/10.1007/s00500-014-1387-5

    Article  MATH  Google Scholar 

  59. Qian Y, Wang Q, Cheng H et al (2015) Fuzzy-rough feature selection accelerator. Fuzzy Set Syst 258:61–78. https://doi.org/10.1016/j.fss.2014.04.029

    Article  MathSciNet  MATH  Google Scholar 

  60. Zhao Z, Liu H (2009) Searching for interacting features in subset selection. Intelligent Data Analysis 13(2):207–228. https://doi.org/10.3233/IDA-2009-0364

    Article  Google Scholar 

  61. Yu L, Liu H (2003) Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Proceedings of the 20th international conference on machine learning (ICML-03), pp 856–863

  62. Paul A, Sil J, Mukhopadhyay CD (2017) Gene selection for designing optimal fuzzy rule base classifier by estimating missing value. Appl Soft Comput 55:276–288. https://doi.org/10.1016/j.asoc.2017.01.046

    Article  Google Scholar 

  63. Hall MA (1999) Correlation-based feature selection for machine learning. Dissertation, University of Tese

  64. Priya RD, Sivaraj R (2017) Dynamic genetic algorithm-based feature selection and incomplete value imputation for microarray classification. Curr Sci, pp 126–131. https://www.jstor.org/stable/24911624

  65. Priya RD, Kuppuswami S (2012) A genetic algorithm based approach for imputing missing discrete attribute values in databases. WSEAS Trans Inf Sci Appl 9(6):169–178

    Google Scholar 

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

    Article  Google Scholar 

  67. Moghaddam VH, Hamidzadeh J (2016) New hermite orthogonal polynomial kernel and combined kernels in support vector machine classifier. Pattern Recogn 60:921–935. https://doi.org/10.1016/j.patcog.2016.07.004

    Article  MATH  Google Scholar 

Download references

Acknowledgements

The paper is supported in part by the National Natural Science Foundation of China under Grant (61976082, 62076089, 62002103).

Author information

Authors and Affiliations

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

    Jiucheng Xu, Xiangru Meng, Kanglin Qu, Yuanhao Sun & Qinchen Hou

  2. Engineering Lab of Intelligence Business & Internet of Things, Henan Province, Xinxiang, 453007, China

    Jiucheng Xu, Xiangru Meng, Kanglin Qu, Yuanhao Sun & Qinchen Hou

Authors
  1. Jiucheng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangru Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kanglin Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanhao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qinchen Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Jiucheng Xu; Methodology: Xiangru Meng; Writing - original draft preparation: Xiangru Meng, Kanglin Qu, Yuanhao Sun; Writing - review and editing: Yuanhao Sun, Qinchen Hou; Funding acquisition: Jiucheng Xu.

Corresponding authors

Correspondence to Xiangru Meng or Kanglin Qu.

Ethics declarations

Competing interests

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

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

Xu, J., Meng, X., Qu, K. et al. Feature selection using relative dependency complement mutual information in fitting fuzzy rough set model. Appl Intell 53, 18239–18262 (2023). https://doi.org/10.1007/s10489-022-04445-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-04445-9

Keywords

Search

Navigation