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Data-guided multi-granularity selector for attribute reduction

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Abstract

Presently, the greedy searching strategy has been widely accepted for obtaining reduct in the field of rough set. In the framework of greedy searching, the evaluation of the candidate attribute is crucial, because the evaluation can determine the final result of reduct to a large extent. However, most of the previous evaluations are designed by considering one and only one fixed granularity, which fails to make the multi-view based evaluation possible. To fill such gap, a Parameterized Multi-granularity Attribute Selector is proposed for obtaining reduct in this paper. Our attribute selector consists of two parts: one is the multi-granularity attribute selector which evaluates and selects attributes through using the information provided by multiple different granularities; the other is the data-guided parameterized granularity selector which generates multiple different parameterized granularities through taking the characteristics of data into account. The experimental results over 15 UCI data sets show the following: 1) compared with the state of the art approaches for obtaining reducts, our proposed attribute selector can contribute to reduct with higher stability; 2) our proposed attribute selector will not provide the reduct with poorer classification performance. This research suggests a new trend for the multi-granularity mechanism in the problem of attribute reduction.

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References

  1. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27

    Article  Google Scholar 

  2. Chen HM, Li TR, Luo C, Horng SJ, Wang GY (2015) A decision-theoretic rough set approach for dynamic data mining. IEEE Trans Fuzzy Syst 23:1958–1970

    Article  Google Scholar 

  3. Chen Y, Liu KY, Song JJ, Fujita H, Yang XB, Qian YH (2020) Attribute group for attribute reduction. Inform Sci 535:64–80

    Article  Google Scholar 

  4. Chen Y, Song JJ, Liu KY, Lin YJ, Yang XB (2020) Combined accelerator for attribute reduction: a sample perspective. Math Probl Eng 2350627:2020

    Google Scholar 

  5. Chen DG, Yang YY, Dong Z (2016) An incremental algorithm for attribute reduction with variable precision rough sets. Appl Soft Comput 45:129–149

    Article  Google Scholar 

  6. Fujita H, Gaeta A, Loia V, Orciuoli F (2019) Resilience analysis of critical infrastructures: a cognitive approach based on granular computing. IEEE Transactions on Cybernetics 49:1835– 1848

    Article  Google Scholar 

  7. Fujita H, Gaeta A, Loia V, Orciuoli F (2020) Hypotheses analysis and assessment in counterterrorism activities: a method based on OWA and fuzzy probabilistic rough sets. IEEE Trans Fuzzy Syst 28:831–845

    Article  Google Scholar 

  8. Fan J, Jiang YL, Liu Y (2017) Quick attribute reduction with generalized indiscernibility models. Information Scineces 397-398:15–36

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Ju HR, Pedrycz W, Li HX, Ding WP, Yang XB, Zhou XZ (2019) Sequential three-way classifier with justifiable granularity. Knoweldege-Based Systems 163:103–119

    Article  Google Scholar 

  11. Jiang ZH, Liu KY, Yang XB, Yu HL, Fujita H, Qian YH (2020) Accelerator for supervised neighborhood based attribute reduction. Int J Approx Reason 119:122–150

    Article  MathSciNet  MATH  Google Scholar 

  12. Jia XY, Rao Y, Shang L, Li TJ (2020) Similarity-based attribute reduction in rough set theory: a clustering perspective. Int J Mach Learn Cybern 11:1047–1060

    Article  Google Scholar 

  13. Jiang GX, Wang WJ (2017) Error estimation based on variation analysis of k-fold cross-validation. Pattern Recogn 69:94–106

    Article  Google Scholar 

  14. Jiang ZH, Yang XB, Yu HL, Liu D, Wang PX, Qian YH (2019) Accelerator for multi-granularity attribute reduction. Knowl.-Based Syst 177:145–158

    Article  Google Scholar 

  15. Ju HR, Yang XB, Yu HL, Li TJ, Yu DJ, Yang JY (2016) Cost-sensitive rough set approach. Inform Sci 355-356:282–298

    Article  MATH  Google Scholar 

  16. Li JH, Ren Y, Mei CL, Qian YH, Yang XB (2016) A comparative study of multigranulation rough sets and concept lettices via rule acquisition. Knowl.-Based Syst 91:152–164

    Article  Google Scholar 

  17. Liang JY, Shi ZZ (2004) The information entropy, rough entropy and knowledge granulation in rough set theory, International Journal of Uncertainty. Fuzziness Knowl.-Based Syst 12:37–46

    Article  MATH  Google Scholar 

  18. Liu KY, Song JJ, Zhang WD, Yang XB (2018) Alleviating over-fitting in attribute reduction: an early stopping strategy. In: Proceedings of the 2018 International conference on wavelet analysis and pattern recognition, Chengdu, pp 190–195

  19. Li Y, Si J, Zhou GJ, Huang SS, Chen SC (2015) FREL: a stable feature selection algorithm. IEEE Trans Neural Netw Learning Sys 26:1388–1402

    Article  MathSciNet  Google Scholar 

  20. Liu KY, Yang XB, Fujita H, Liu D, Yang X, Qian YH (2019) An efficient selector for multi-granularity attribute reduction. Inform Sci 505:457–472

    Article  Google Scholar 

  21. Li JZ, Yang XB, Song XN, Li JH, Wang PX, Yu DJ (2019) Neighborhood attribute reduction: a multi-criterion approach. Int J Mach Learn Cybern 10:731–742

    Article  Google Scholar 

  22. Liu KY, Yang X, Yu HL, Chen XJ (2020) Supervised information granulation strategy for attribute reduction. International Journal of Machine Learning and Cybernetics, https://doi.org/10.1007/s13042-020-01107-5

  23. Liu KY, Yang XB, Yu HL, Mi JS, Wang PX, Chen XJ (2019) Rough set based semi-supervised feature selection via ensemble selector. Knowl.-Based Syst 165:282–296

    Article  Google Scholar 

  24. Liao SJ, Zhu QX, Qian YH, Lin GP (2018) Multi-granularity feature selection on cost-sensitive data with measurement errors and variable costs. Knowl.-Based Syst 158:25–42

    Article  Google Scholar 

  25. Min F, He HP, Qian YH, Zhu W (2011) Test-cost-sensitive attribute reduction. Inform Sci 181:4928–4942

    Article  Google Scholar 

  26. Maji P, Garai P (2013) On fuzzy-rough attribute selection: criteria of max-dependency, max-relevance, max-redundancy, and max-significance. Appl Soft Comput 13:3968–3980

    Article  Google Scholar 

  27. Pandiri V, Singh A (2018) A swarm intelligence approach for the colored treveling salesman problem. Appl Intell 48:4412–4428

    Article  Google Scholar 

  28. Pedrycz W, Succi G, Sillitti A, Iljazi J (2015) Data description: a general framework of information granules. Knowl.-Based Syst 80:98–108

    Article  Google Scholar 

  29. Qian YH, Cheng HH, Wang JT, Liang JY, Pedrycz W, Dang CY (2017) Grouping granular structures in human granulation intelligence. Inform Sci 382-383:150–169

    Article  Google Scholar 

  30. Qian YH, Liang JY, Dang CY (2009) Knowledge structure, knowledge granulation and knowledge distance in a knowledge base. Int J Approx Reason 50:174–188

    Article  MathSciNet  MATH  Google Scholar 

  31. Qian YH, Liang JY, Pedrycz W, Dang CY (2010) Positive approximation: an accelerator for attribute reduction in rough set theory. Artif Intell 174:597–618

    Article  MathSciNet  MATH  Google Scholar 

  32. Rao XS, Yang XB, Yang X, Chen XJ, Liu D, Qian YH (2020) Quickly calculating reduct: an attribute relationship based approach. Knowl.-Based Syst 106014:200

    Google Scholar 

  33. She YH, He XL, Qian T, Wang QQ, Zeng WL (2019) A theoretical study on object-oriented and property-oriented multi-scale formal concept analysis. Int J Mach Learn Cybern 10:3263–3271

    Article  Google Scholar 

  34. Skowron A, Jankowski A (2016) Rough sets and interactive granular computing. Fundamenta Informaticae 147:371– 385

    Article  MathSciNet  MATH  Google Scholar 

  35. Skowron A, Polkowski L (1998) Rough mereological foundations for design, analysis, synthesis, and control in distributed systems. Inform Sci 104:129–156

    Article  MathSciNet  MATH  Google Scholar 

  36. Song JJ, Tsang ECC, Chen DG, Yang XB (2017) Minimal decision cost reduct in fuzzy decision-theoretic rough set model. Knowl.-Based Syst 126:104–112

    Article  Google Scholar 

  37. Tsang ECC, Hu QH, Chen DG (2016) Feature and instance reduction for pnn classifiers based on fuzzy rough sets. Int J Mach Learn Cybern 7:1–11

    Article  Google Scholar 

  38. Tsang ECC, Song JJ, Chen DG, Yang XB (2019) Order based hierarchies on hesitant fuzzy approximation space. Int J Mach Learn Cybern 10:1407–1422

    Article  Google Scholar 

  39. 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 

  40. Wang CZ, Hu QH, Wang XZ, Chen DG, Qian YH, Dong Z (2018) Feature selection based on neighborhood discrimination index. IEEE Trans Neural Netw Learning Sys 29:2986–2999

    MathSciNet  Google Scholar 

  41. Wu WZ, Leung Y (2019) A comparison study of optimal scale combination selection in generalized multi-scale decision tables. Int J Mach Learn Cybern 12:1–12

    Google Scholar 

  42. Wei W, Liang JY (2019) Information fusion in rough set theory: an overview. Information Fusion 48:107–118

    Article  Google Scholar 

  43. Wu WZ, Qian YH, Li TJ, Gu SM (2017) On rule acquisition in incomplete muulti-scale decision tables. Inform Sci 378:282–302

    Article  MathSciNet  MATH  Google Scholar 

  44. Wang CZ, Shi YP, Fan XD, Shao MW (2019) Attribute reduction based on k-nearest neighborhood rough sets. Int J Approx Reason 106:18–31

    Article  MathSciNet  MATH  Google Scholar 

  45. Xu SP, Ju HR, Shang L, Pedrycz W, Yang XB, Li C (2020) Label distribution learning: a local collaborative mechanism. Int J Approx Reason 121:59–84

    Article  MathSciNet  Google Scholar 

  46. Xu WH, Li WT (2016) Granular computing appraoch to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Transactions on Cybernetics 46:366–379

    Article  Google Scholar 

  47. Xu WH, Yu JH (2017) A novel approach to information fusion in multi-source datasets: a granular computing viewpoint. Inform Sci 378:410–423

    Article  MATH  Google Scholar 

  48. Xu SP, Yang XB, Yu HL, Yu DJ, Yang JY, Tsang ECC (2016) Multi-label learning with label-specific feature reduction. Knowl.-Based Syst 104:52–61

    Article  Google Scholar 

  49. Yao YY (2020) Tri-level thinking: models of three-way decision. Int J Mach Learn Cybern 11:947–959

    Article  Google Scholar 

  50. Yang XB, Liang SC, Yu HL, Gao S, Qian YH (2019) Pseudo-label neighborhood rough set: measures and attribute reductions. Int J Approx Reason 105:112–129

    Article  MathSciNet  MATH  Google Scholar 

  51. Yang XB, Qi YS, Song XN, Yang JY (2013) Test cost sensitive multigranulation rough set: model and minimal cost selection. Inform Sci 250:184–199

    Article  MathSciNet  MATH  Google Scholar 

  52. Yang XB, Yao YY (2018) Ensemble selector for attribute reduction. Appl Soft Comput 70:1–11

    Article  Google Scholar 

  53. Yao YY, Zhao Y (2009) Discernibility matrix simplification for constructing attribute reducts. Inform Sci 179:867–882

    Article  MathSciNet  MATH  Google Scholar 

  54. Yao YY, Zhao Y, Wang J (2008) On reduct construction algorithms. Transactions on Computational Science II 5150:100–117

    MATH  Google Scholar 

  55. Zhu PF, Hu QH, Zuo WM, Yang M, Yang M (2014) Multi-granularity distance metric learning via neighborhood granule margin maximization. Inform Sci 282:321–331

    Article  Google Scholar 

  56. Zhang QH, Lv GX, Chen YH, Wang GY (2018) A dynamic three-way decision model based on the updating of attribute values. Knowl Based-Sys 142:71–84

    Article  Google Scholar 

  57. 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 Recogn 56:1–15

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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Acknowledgements

This work is supported by the Natural Science Foundation of China (No. 61906078), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX20_3162) and the Key Laboratory of Data Science and Intelligence Application, Fujian Province University (No. D1901).

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Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212003, China

    Zehua Jiang, Huili Dou, Jingjing Song & Xibei Yang

  2. Key Laboratory of Data Science and Intelligence Application, Zhangzhou, Fujian, 363000, China

    Zehua Jiang, Huili Dou, Jingjing Song & Xibei Yang

  3. School of Science, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212003, China

    Pingxin Wang

  4. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212003, China

    Xibei Yang

  5. Institute of Big Data Science and Industry, Shanxi University, Taiyuan, Shanxi, 030006, China

    Yuhua Qian

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  1. Zehua Jiang
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Correspondence to Huili Dou.

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Jiang, Z., Dou, H., Song, J. et al. Data-guided multi-granularity selector for attribute reduction. Appl Intell 51, 876–888 (2021). https://doi.org/10.1007/s10489-020-01846-6

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