iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S10489-024-05604-W
Unsupervised attribute reduction based on neighborhood dependency | Applied Intelligence Skip to main content
Springer Nature Link
Log in

Unsupervised attribute reduction based on neighborhood dependency

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Neighborhood rough set theory is an important computational model in granular computing and has been successfully applied in many areas. One of its most prominent applications is in attribute reduction. However, most current attribute reduction methods for neighborhood rough sets are supervised or semi-supervised, which makes them unable to handle datasets without decision information. To address this, we propose an unsupervised attribute reduction strategy based on neighborhood dependency. First, a neighborhood rough set model based on conditional attribute sets is constructed. Then, based on all individual attribute subsets in the datasets, the importance of the attributes is defined to indicate the significance of the candidate attributes. Furthermore, a neighborhood dependency-based unsupervised attribute reduction (NDUAR) algorithm is designed. Finally, NDUAR is compared with existing algorithms on publicly available datasets. The experimental results show that NDUAR can select fewer attributes to maintain or improve the performance of the clustering algorithm. The effectiveness of the algorithm proposed in this paper is thereby confirmed.

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

Similar content being viewed by others

Explore related subjects

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

References

  1. Wang HJ, Zhang YH, Zhang J, Li TR, Peng LX (2019) A factor graph model for unsupervised feature selection. Inf Sci 480:144–159

    Article  MathSciNet  Google Scholar 

  2. Zhao JD, Lu K, He XF (2008) Locality sensitive semi-supervised feature selection. Neurocomputing 71(10–12):1842–1849

    Article  Google Scholar 

  3. Zhu PF, Xu Q, Hu QH, Zhang CQ (2018) Co-regularized unsupervised feature selection. Neurocomputing 275:2855–2863

    Article  Google Scholar 

  4. Yuan Z, Chen HM, Li TR, Yu Z, Sang BB, Luo C (2021) Unsupervised attribute reduction for mixed data based on fuzzy rough sets. Inf Sci 572:67–87

    Article  MathSciNet  Google Scholar 

  5. Pal SK, Mitra P (2004) Pattern recognition algorithms for data mining. Chapman and Hall/CRC

  6. Kotsiantis SB (2011) Feature selection for machine learning classification problems: a recent overview. Artif Intell Rev 42:157–176

    Article  Google Scholar 

  7. Tang J, Alelyani S, Liu H (2014) Feature selection for classification: a review. Data Classif: Algorithms Appl 37

  8. Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2017) A survey on semi-supervised feature selection methods. Pattern Recognit 64:141–158

    Article  Google Scholar 

  9. Alelyani S, Tang J, Liu H (2018) Feature selection for clustering: a review. Data Cluster 29–60

  10. Solorio-Fernández S, Carrasco-Ochoa JA, Martínez-Trinidad JF (2020) A review of unsupervised feature selection methods. Artif Intell Rev 53(2):907–948

    Article  Google Scholar 

  11. Dai JH, Hu H, Wu WZ, Qian YH, Huang DB (2017) Maximal-discernibility-pair-based approach to attribute reduction in fuzzy rough sets. IEEE Trans Fuzzy Syst 26(4):2174–2187

    Article  Google Scholar 

  12. Wang X, Tsang EC, Zhao S, Chen D, Yeung DS (2007) Learning fuzzy rules from fuzzy samples based on rough set technique. Inf Sci 177(20):4493–4514

    Article  MathSciNet  Google Scholar 

  13. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356

    Article  Google Scholar 

  14. Teng SH, Lu M, Yang AF, Zhang J, Nian YJ, He M (2016) Efficient attribute reduction from the viewpoint of discernibility. Inf Sci 326:297–314

    Article  MathSciNet  Google Scholar 

  15. Zhang X, Mei CL, Chen DG, Li JH (2016) Feature selection in mixed data: a method using a novel fuzzy rough set-based information entropy. Pattern Recognit 56:1–15

    Article  Google Scholar 

  16. Lin T (1988) Neighborhood systems and relational database. abstract. In: Proceedings of CSC, vol 88, p 725

  17. Hu QH, Yu D, Liu JF, Wu CX (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594

  18. Hu QH, Liu JF, Yu DR (2008) Mixed feature selection based on granulation and approximation. Knowl-Based Syst 21(4):294–304

    Article  Google Scholar 

  19. Yong L, Huang WL, YunLiang J, Yong ZZ (2014) Quick attribute reduct algorithm for neighborhood rough set model. Inf Sci 271:65–81

    Article  MathSciNet  Google Scholar 

  20. Yao P, Lu YH (2011) Neighborhood rough set and svm based hybrid credit scoring classifier. Expert Syst Appl 38(9):11300–11304

    Article  Google Scholar 

  21. Meng J, Zhang J, Luan YS (2014) Gene selection integrated with biological knowledge for plant stress response using neighborhood system and rough set theory. IEEE/ACM Trans Comput Biol Bioinform 12(2):433–444

    Article  Google Scholar 

  22. Zhao J, Liang JM, Dong ZN, Tang DY, Liu Z (2020) Nec: a nested equivalence class-based dependency calculation approach for fast feature selection using rough set theory. Inf Sci 536:431–453

    Article  MathSciNet  Google Scholar 

  23. Wang CZ, He Q, Shao MW, Hu QH (2018) Feature selection based on maximal neighborhood discernibility. Int J Mach Learn Cybern 9:1929–1940

    Article  Google Scholar 

  24. Hu CX, Zhang L, Wang BJ, Zhang Z, Li FZ (2019) Incremental updating knowledge in neighborhood multigranulation rough sets under dynamic granular structures. Knowl-Based Syst 163:811–829

    Article  Google Scholar 

  25. Sun L, Zhang XY, Qian YH, Xu JC, Zhang SG (2019) Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification. Inf Sci 502:18–41

    Article  MathSciNet  Google Scholar 

  26. Wang CZ, Huang Y, Shao MW, Hu QH, Chen DG (2019) Feature selection based on neighborhood self-information. IEEE Trans Cybern 50(9):4031–4042

    Article  Google Scholar 

  27. Xu JC, Qu KL, Yuan M, Yang J (2021) Feature selection combining information theory view and algebraic view in the neighborhood decision system. Entropy 23(6):704

    Article  MathSciNet  Google Scholar 

  28. Mitra P, Murthy C, Pal SK (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3):301–312

    Article  Google Scholar 

  29. He X, Cai D, Niyogi P (2018) Laplacian score for feature selection. Adv Neural Inf Process Syst 18

  30. Tabakhi S, Moradi P, Akhlaghian F (2014) An unsupervised feature selection algorithm based on ant colony optimization. Eng Appl Artif Intell 32:112–123

    Article  Google Scholar 

  31. Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res 5(Aug):845–889

  32. Dutta D, Dutta P, Sil J (2014) Simultaneous feature selection and clustering with mixed features by multi objective genetic algorithm. Int J Hybrid Intell Syst 11(1):41–54

    Google Scholar 

  33. Law MH, Figueiredo MA, Jain AK (2004) Simultaneous feature selection and clustering using mixture models. IEEE Trans Pattern Anal Mach Intell 26(9):1154–1166

    Article  Google Scholar 

  34. Dash M, Liu H (2000) Feature selection for clustering. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 110–121

  35. Solorio-Fernández S, Carrasco-Ochoa JA, Martínez-Trinidad JF (2016) A new hybrid filter-wrapper feature selection method for clustering based on ranking. Neurocomputing 214:866–880

    Article  Google Scholar 

  36. Hruschka ER, Covoes TF, Ebecken NF (2005) Feature selection for clustering problems: a hybrid algorithm that iterates between k-means and a bayesian filter. In: Fifth international conference on hybrid intelligent systems (HIS’05), IEEE, p 6

  37. Dong LJ, Gang CD, Ling WN, Hui LZ (2020) Key energy-consumption feature selection of thermal power systems based on robust attribute reduction with rough sets. Inf Sci 532:61–71

    Article  Google Scholar 

  38. Zhu PF, Hu QH, Han YH, Zhang CQ, Du Y (2016) Combining neighborhood separable subspaces for classification via sparsity regularized optimization. Inf Sci 370:270–287

    Article  Google Scholar 

  39. Liu J, Lin Y, Li Y et al (2018) Online multi-label streaming feature selection based on neighborhood rough set. Pattern Recognit 84:273–287

    Article  Google Scholar 

  40. Li LJ, Li MZ, Mi JS, Xie B (2020) Dynamic granularity selection based on local weighted accuracy and local likelihood ratio. Appl Soft Comput 89:106087

    Article  Google Scholar 

  41. Sun L, Wang LY, Ding WP, Qian YH, Xu JC (2020) Feature selection using fuzzy neighborhood entropy-based uncertainty measures for fuzzy neighborhood multigranulation rough sets. IEEE Trans Fuzzy Syst 29(1):19–33

    Article  Google Scholar 

  42. Xu JC, Yuan M, Ma YY (2022) Feature selection using self-information and entropy-based uncertainty measure for fuzzy neighborhood rough set. Complex Intell Syst 8(1):287–305

    Article  Google Scholar 

  43. Wan JH, Chen HM, Yuan Z, Li TR, Yang XL, Sang BB (2021) A novel hybrid feature selection method considering feature interaction in neighborhood rough set. Knowl-Based Syst 227:107167

    Article  Google Scholar 

  44. Yang XL, Chen HM, Li TR, Wan JH, Sang BB (2021) Neighborhood rough sets with distance metric learning for feature selection. Knowl-Based Syst 224:107076

    Article  Google Scholar 

  45. Hu QH, Yu DR, Xie ZX (2008) Neighborhood classifiers. Expert Syst Appl 34(2):866–876

    Article  Google Scholar 

  46. Hu QH, Liu JF, Yu DR (2008) Mixed feature selection based on granulation and approximation. Knowl-Based Syst 21(4):294–304

    Article  Google Scholar 

  47. Yuan Z, Zhang XY, Feng S (2018) Hybrid data-driven outlier detection based on neighborhood information entropy and its developmental measures. Expert Syst Appl 112:243–257

    Article  Google Scholar 

  48. Yuan Z, Chen HM, Yang XL, Li TR, Liu KY (2021) Fuzzy complementary entropy using hybrid-kernel function and its unsupervised attribute reduction. Knowl-Based Syst 231:107398

    Article  Google Scholar 

  49. Solorio-Fernández S, Martínez-Trinidad JF, Carrasco-Ochoa JA (2017) A new unsupervised spectral feature selection method for mixed data: a filter approach. Pattern Recognit 72:314–326

    Article  Google Scholar 

  50. Parthaláin NM, Jensen R (2013) Unsupervised fuzzy-rough set-based dimensionality reduction. Inf Sci 229:106–121

    Article  MathSciNet  Google Scholar 

  51. Zhao Z, Liu H (2007) Spectral feature selection for supervised and unsupervised learning. In: Proceedings of the 24th international conference on machine learning, pp 1151–1157

  52. Zhu PF, Zuo WM, Zhang L, Hu QH, Shiu SC (2015) Unsupervised feature selection by regularized self-representation. Pattern Recognit 48(2):438–446

    Article  Google Scholar 

  53. Zhang PF, Li TR, Yuan Z, Deng ZX, Wang GQ, Wang DX, Zhang F (2023) A possibilistic information fusion-based unsupervised feature selection method using information quality measures. IEEE Trans Fuzzy Syst

  54. Wang ZH, Chen HM, Yuan Z, Yang XL, Zhang PF, Li TR (2022) Exploiting fuzzy rough mutual information for feature selection. Appl Soft Comput 131:109769

    Article  Google Scholar 

  55. Zhu PF, Zhu WC, Hu QH, Zhang CQ, Zuo WM (2017) Subspace clustering guided unsupervised feature selection. Pattern Recognit 66:364–374

    Article  Google Scholar 

  56. 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  Google Scholar 

  57. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets, The. J Mach Learn Res 7:1–30

    MathSciNet  Google Scholar 

  58. Yuan Z, Chen BY, Liu J, Chen HM, Peng DZ, Li PL (2023) Anomaly detection based on weighted fuzzy-rough density. Appl Soft Comput 134:109995

    Article  Google Scholar 

  59. Yuan Z, Chen HM, Luo C, Peng DZ (2023) Mfgad: multi-fuzzy granules anomaly detection. Inf Fus 95:17–25

    Article  Google Scholar 

  60. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323

    Article  Google Scholar 

  61. Daniels MJ, Normand S-LT (2006) Longitudinal profiling of health care units based on continuous and discrete patient outcomes. Biostatistics 7(1):1–15

  62. Liu HT, Wei RX, Jiang GP (2013) A hybrid feature selection scheme for mixed attributes data. Comput Appl Math 32:145–161

    Article  MathSciNet  Google Scholar 

  63. Yuan Z, Chen HM, Xie P, Zhang PF, Liu J, Li TR (2021) Attribute reduction methods in fuzzy rough set theory: an overview, comparative experiments, and new directions. Appl Soft Comput 107:107353

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank both the editors and reviewers for their valuable suggestions, which substantially improved this paper. This work was supported by the Key Research and Development projects in Sichuan province (2023YFG0303), the Project of Sichuan Provincial Department of Science and Technology (2023ZHCG0009), the Science and technology project in Ganzi Prefecture Sichuan province (23KJJH00016), the Research Team of Sichuan Minzu College (2022TD07), the Natural Science Foundation of Sichuan Province (NO.2022NSFSC1830), and the Southwest Minzu University Research Startup Funds (NO.RQD2022035).

Author information

Authors and Affiliations

  1. College of Intelligent Science and Technology, Sichuan Minzu College, Kangding, 626001, China

    Yi Li, Benwen Zhang, Shenhong Lei & Xingqiang Tan

  2. School of Computer Science, Sichuan University, Chengdu, 610065, China

    Zhong Yuan

  3. School of Mathematics, Southwest Minzu University, Chengdu, 610041, China

    Yuncheng Liu

Authors
  1. Yi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benwen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhong Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuncheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shenhong Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xingqiang Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xingqiang Tan.

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

Li, Y., Zhang, B., Yuan, Z. et al. Unsupervised attribute reduction based on neighborhood dependency. Appl Intell 54, 10653–10670 (2024). https://doi.org/10.1007/s10489-024-05604-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-024-05604-w

Keywords

Search

Navigation