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Unsupervised attribute reduction algorithm framework based on spectral clustering and attribute significance function

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Abstract

Attribute reduction is a significant challenge in fields like data mining and pattern recognition. Various models have been introduced to enhance the performance of attribute reduction algorithms, such as the fuzzy rough sets model. However, the common greedy-based reduction algorithm frameworks shared by these models often struggle to efficiently remove redundant attributes. Manual intervention is often employed by researchers to extract the optimal attribute subset, such as setting hyperparameters to control the algorithm’s progression. Unfortunately, these methods lack practical relevance. To address these challenges, this study presents an unsupervised attribute reduction algorithm framework that employs spectral clustering and an attribute significance function. Initially, we introduce an attribute similarity function and a spectral clustering algorithm to capture the data’s main partition structures. We then propose a method for automatically selecting the optimal clustering result, aiming to generate preliminary reduction outcomes. Additionally, we developed a novel unsupervised attribute reduction framework by integrating it with the traditional approach. Furthermore, a specific unsupervised attribute reduction algorithm has been obtained by embedding an unsupervised attribute significance function. Comparative experiments were conducted with six state-of-the-art algorithms across 27 datasets, and the results show that our proposed algorithm demonstrates superior efficiency and effectiveness in attribute selection.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the UCI repository: http://archive.ics.uci.edu/ml.

References

  1. Liu K, Li T, Yang X, Chen H, Wang J, Deng Z (2023) Semifree: semi-supervised feature selection with fuzzy relevance and redundancy. IEEE Transactions on Fuzzy Systems

  2. Gao Y, Chen D, Wang H (2024) Optimal granularity selection based on algorithm stability with application to attribute reduction in rough set theory. Inf Sci 654:119845

    Article  MATH  Google Scholar 

  3. Liu Y, Gong Z, Liu K, Xu S, Ju H, Yang X (2023) Aq-learning approach to attribute reduction. Appl Intell 53(4):3750–3765

    Article  MATH  Google Scholar 

  4. Li Z, Liu J, Peng Y, Wen C-F (2024) A novel method to information fusion in multi-source incomplete interval-valued data via conditional information entropy: Application to mutual information entropy based attribute reduction. Inf Sci 658:120011

    Article  MATH  Google Scholar 

  5. Ba J, Wang P, Yang X, Yu H, Yu D (2023) Glee: A granularity filter for feature selection. Eng Appl Artif Intell 122:106080

    Article  MATH  Google Scholar 

  6. Li W, Zhai S, Xu W, Pedrycz W, Qian Y, Ding W, Zhan T (2022) Feature selection approach based on improved fuzzy c-means with principle of refined justifiable granularity. IEEE Transactions on Fuzzy Systems

  7. Liu Z, Yang J, Wang L, Chang Y (2023) A novel relation aware wrapper method for feature selection. Pattern Recogn 140:109566

    Article  MATH  Google Scholar 

  8. Shi D, Zhu L, Li J, Zhang Z, Chang X (2023) Unsupervised adaptive feature selection with binary hashing. IEEE Trans Image Process 32:838–853

    Article  MATH  Google Scholar 

  9. Dai J, Wang Z, Huang W (2023) Interval-valued fuzzy discernibility pair approach for attribute reduction in incomplete interval-valued information systems. Inf Sci 642:119215

    Article  MATH  Google Scholar 

  10. Zhang P, Li T, Wang G, Luo C, Chen H, Zhang J, Wang D, Yu Z (2021) Multi-source information fusion based on rough set theory: A review. Information Fusion 68:85–117

    Article  MATH  Google Scholar 

  11. Zhang X, Yao H, Lv Z, Miao D (2021) Class-specific information measures and attribute reducts for hierarchy and systematicness. Inf Sci 563:196–225

    Article  MathSciNet  MATH  Google Scholar 

  12. Chen Y, Yang X, Li J, Wang P, Qian Y (2022) Fusing attribute reduction accelerators. Inf Sci 587:354–370

    Article  MATH  Google Scholar 

  13. Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. International Journal of General System 17(2–3):191–209

    Article  MATH  Google Scholar 

  14. Dubois D, Prade H (1992) Putting rough sets and fuzzy sets together. Intelligent decision support: Handbook of applications and advances of the rough sets theory 203–232

  15. He J, Zhang G, Huang D, Wang P, Yu G (2023) Measures of uncertainty for partially labeled categorical data based on an indiscernibility relation: an application in semi-supervised attribute reduction. Appl Intell 53(23):29486–29513

    Article  MATH  Google Scholar 

  16. Chen J, Zhu P (2023) A multigranulation rough set model based on variable precision neighborhood and its applications. Appl Intell 53(21):24822–24846

    Article  MATH  Google Scholar 

  17. Wang C, Qian Y, Ding W, Fan X (2021) Feature selection with fuzzy-rough minimum classification error criterion. IEEE Trans Fuzzy Syst 30(8):2930–2942

    Article  MATH  Google Scholar 

  18. Wang C, Huang Y, Ding W, Cao Z (2021) Attribute reduction with fuzzy rough self-information measures. Inf Sci 549:68–86

    Article  MathSciNet  MATH  Google Scholar 

  19. Li Z, Huang H, Huang Q, Lin Y (2024) Attribute reduction for hybrid data based on statistical distribution of data and fuzzy evidence theory. Inf Sci 662:120247

    Article  MATH  Google Scholar 

  20. Wang Z, Zhang X (2023) The granulation attribute reduction of multi-label data. Applied Intelligence 1–19

  21. Ali G, Afzal M, Asif M, Shazad A (2021) Attribute reduction approaches under interval-valued q-rung orthopair fuzzy soft framework. Applied intelligence 1–26

  22. Jiang Z, Dou H, Song J, Wang P, Yang X, Qian Y (2021) Data-guided multi-granularity selector for attribute reduction. Appl Intell 51:876–888

    Article  MATH  Google Scholar 

  23. Hu M, Guo Y, Chen D, Tsang EC, Zhang Q (2023) Attribute reduction based on neighborhood constrained fuzzy rough sets. Knowl-Based Syst 274:110632

    Article  MATH  Google Scholar 

  24. Xie L, Lin G, Li J, Lin Y (2023) A novel fuzzy-rough attribute reduction approach via local information entropy. Fuzzy Sets Syst 473:108733

    Article  MathSciNet  MATH  Google Scholar 

  25. Wang P, He J, Li Z (2023) Attribute reduction for hybrid data based on fuzzy rough iterative computation model. Inf Sci 632:555–575

    Article  MATH  Google Scholar 

  26. Yao Y, Zhao Y, Wang J (2008) On reduct construction algorithms. Transactions on computational science II 100–117

  27. Yang T, Li YJ, Qian Y, Wang FY (2023) Consistent matrix: A feature selection framework for large-scale data sets. IEEE Transactions on Fuzzy Systems

  28. Yang X, Li M, Fujita H, Liu D, Li T (2022) Incremental rough reduction with stable attribute group. Inf Sci 589:283–299

    Article  MATH  Google Scholar 

  29. Chen Z, Liu K, Yang X, Fujita H (2022) Random sampling accelerator for attribute reduction. Int J Approximate Reasoning 140:75–91

    Article  MathSciNet  MATH  Google Scholar 

  30. Yuan Z, Chen H, Li T, Yu Z, Sang B, Luo C (2021) Unsupervised attribute reduction for mixed data based on fuzzy rough sets. Inf Sci 572:67–87

    Article  MathSciNet  MATH  Google Scholar 

  31. Wen H, Zhao S, Liang M (2023) Unsupervised attribute reduction algorithm for mixed data based on fuzzy optimal approximation set. Mathematics 11(16):3452

    Article  MATH  Google Scholar 

  32. Yuan Z, Chen H, Li T (2022) Exploring interactive attribute reduction via fuzzy complementary entropy for unlabeled mixed data. Pattern Recogn 127:108651

    Article  MATH  Google Scholar 

  33. Yuan Z, Chen H, Yang X, Li T, Liu K (2021) Fuzzy complementary entropy using hybrid-kernel function and its unsupervised attribute reduction. Knowl-Based Syst 231:107398

    Article  MATH  Google Scholar 

  34. Xu W, Huang M, Jiang Z, Qian Y (2023) Graph-based unsupervised feature selection for interval-valued information system. IEEE Transactions on Neural Networks and Learning Systems

  35. Saerens M, Fouss F, Yen L, Dupont P (2004) The principal components analysis of a graph, and its relationships to spectral clustering. In: ECML, vol. 3201, pp. 371–383. Springer

  36. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  MATH  Google Scholar 

  37. Suganya R, Shanthi R (2012) Fuzzy c-means algorithm-a review. Int J Sci Res Publ 2(11):1

    MATH  Google Scholar 

  38. Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17:395–416

    Article  MathSciNet  MATH  Google Scholar 

  39. Chung FR (1997) Spectral Graph Theory, vol 92. American Mathematical Soc, Washington

    MATH  Google Scholar 

  40. Yeung DS, Chen D, Tsang EC, Lee JW, Xizhao W (2005) On the generalization of fuzzy rough sets. IEEE Trans Fuzzy Syst 13(3):343–361

    Article  MATH  Google Scholar 

  41. Hu Q, Yu D, Pedrycz W, Chen D (2010) Kernelized fuzzy rough sets and their applications. IEEE Trans Knowl Data Eng 23(11):1649–1667

    Article  MATH  Google Scholar 

  42. Yuan Z, Chen H, Xie P, Zhang P, Liu J, Li T (2021) Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions. Appl Soft Comput 107:107353

    Article  MATH  Google Scholar 

  43. Yang X, Yao Y (2018) Ensemble selector for attribute reduction. Appl Soft Comput 70:1–11

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  45. Dheeru D, Taniskidou EK (2017) Uci machine learning repository

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

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  48. Velayutham C, Thangavel K (2011) Unsupervised quick reduct algorithm using rough set theory. Journal of electronic science and technology 9(3):193–201

  49. Velayutham C, Thangavel K (2012) A novel entropy based unsupervised feature selection algorithm using rough set theory. In: IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM-2012), pp. 156–161. IEEE

  50. Zhu P, Zhu W, Hu Q, Zhang C, Zuo W (2017) Subspace clustering guided unsupervised feature selection. Pattern Recogn 66:364–374

    Article  MATH  Google Scholar 

  51. 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 

  52. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. The Journal of Machine learning research 7:1–30

    MathSciNet  MATH  Google Scholar 

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Funding

This study was funded by the National Natural Science Foundation of China (No. 62076088, 72101082).

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Correspondence to Meishe Liang.

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Wen, H., Liang, M., Zhao, S. et al. Unsupervised attribute reduction algorithm framework based on spectral clustering and attribute significance function. Appl Intell 55, 64 (2025). https://doi.org/10.1007/s10489-024-05878-0

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