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Supervised information granulation strategy for attribute reduction

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Abstract

In rough set based Granular Computing, neighborhood relation has been widely accepted as one of the most popular approaches for realizing information granulation. Such approach is to group samples in terms of their similarities without the consideration of their labels. Therefore, it can be referred to as an unsupervised information granulation strategy. Nevertheless, it is obvious that such unsupervised mechanism may generate imprecise neighborhoods by comparing the actual labels of samples. It follows that it is not good enough for classification-oriented attribute reduction to select qualified attributes. To fill such a gap, a novel supervised information granulation strategy is proposed. Different from the unsupervised information granulation, samples are grouped by using not only the similarities over conditional attributes but also the labels. For such a purpose, our mechanism mainly contains two aspects: (1) intra-class radius, which aims to add samples with the same label into neighborhood; (2) extra-class radius, which aims to delete samples with different labels from the neighborhood. The experimental results over 12 UCI data sets demonstrate that, compared with previous researches, the reducts derived by our supervised information granulation may contribute to superior classification performances. This study suggests new trends and applications of considering information granulation from the viewpoint of supervised learning.

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References

  1. Chen DG, Li WL, Zhang X, Kwong S (2014) Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets. Int J Approx Reason 55:908–923

    MathSciNet  MATH  Google Scholar 

  2. Chen DG, Yang YY (2014) Attribute reduction for heterogeneous data based on the combination of classical and fuzzy rough set models. IEEE Trans Fuzzy Syst 22:1325–1334

    Google Scholar 

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

    Google Scholar 

  4. Dai JH, Hu H, Wu WZ, Qian YH, Huang DB (2018) Maximal-discernibility-pair-based approach to attribute reduction in fuzzy rough sets. IEEE Trans Fuzzy Syst 26:2174–2187

    Google Scholar 

  5. Dai JH, Hu QH, Hu H, Huang DB (2018) Neighbor inconsistent pair selection for attribute reduction by rough set approach. IEEE Trans Fuzzy Syst 26:937–950

    Google Scholar 

  6. Dai JH, Hu QH, Zhang JH, Hu H, Zheng NG (2017) Attribute selection for partially labeled categorical data by rough set approach. IEEE Trans Cybern 47:2460–2471

    Google Scholar 

  7. Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gener Syst 17:191–209

    MATH  Google Scholar 

  8. Fujita H, Gaeta A, Loia V, Orciuoli F (2019) Improving awareness in early stages of security analysis: a zone partition method based on GrC. Appl Intell 49:1063–1077

    Google Scholar 

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

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  11. Hu QH, Yu DR, Pedrycz W, Chen DG (2011) Kernelized fuzzy rough sets and their applications. IEEE Trans Knowl Data Eng 23:1649–1667

    Google Scholar 

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

    Google Scholar 

  13. Hu QH, Zhang L, Chen DG, Pedrycz W, Yu DR (2010) Gaussian kernel based fuzzy rough sets: model, uncertainty measures and applications. Int J Approx Reason 51:453–471

    MATH  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

    Google Scholar 

  15. Ju HR, Li HX, Yang XB, Zhou XZ, Huang B (2017) Cost-sensitive rough set: a multi-granulation approach. Knowl Based Syst 123:137–153

    Google Scholar 

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

    MATH  Google Scholar 

  17. Li JH, Huang CC, Qi JJ, Qian YH, Liu WQ (2017) Three-way cognitive concept learning via multi-granularity. Inf Sci 378:244–263

    MATH  Google Scholar 

  18. Li JH, Mei CL, Xu WH, Qian YH (2015) Concept learning via granular computing: a cognitive viewpoint. Inf Sci 298:447–467

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  20. 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

    Google Scholar 

  21. Li KW, Shao MW, Wu WZ (2017) A data reduction method in formal fuzzy contexts. Int J Mach Learn Cybern 8:1145–1155

    Google Scholar 

  22. Liu D, Li TR, Liang DC (2014) Incorporating logistic regression to decision-theoretic rough sets for classifications. Int J Approx Reason 55:197–210

    MathSciNet  MATH  Google Scholar 

  23. Liu D, Li TR, Ruan D (2011) Probabilistic model criteria with decision-theoretic rough sets. Inf Sci 181:3709–3722

    MathSciNet  Google Scholar 

  24. Liu JH, Lin YJ, Lin ML, Wu SX, Zhang J (2017) Feature selection based on quality of information. Neurocomputing 225:11–22

    Google Scholar 

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

    Google Scholar 

  26. 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

    Google Scholar 

  27. Pawlak Z (1992) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Berlin

    MATH  Google Scholar 

  28. Pedrycz W (2015) From fuzzy data analysis and fuzzy regression to granular fuzzy data analysis. Fuzzy Sets Syst 274:12–17

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  30. Qian YH, Zhang H, Li FJ, Hu QH, Liang JY (2014) Set-based granular computing: a lattice model. Int J Approx Reason 55:834–852

    MathSciNet  MATH  Google Scholar 

  31. Śļezak D (2002) Approximate entropy reducts. Fundamenta Informaticae 53:365–390

    MathSciNet  Google Scholar 

  32. Shao MW, Leung Y, Wang XZ, Wu WZ (2016) Granular reducts of formal fuzzy contexts. Knowl Based Syst 114:156–166

    Google Scholar 

  33. Shao MW, Li KW (2017) Attribute reduction in generalized one-sided formal contexts. Inf Sci 378:317–327

    MathSciNet  MATH  Google Scholar 

  34. 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

    Google Scholar 

  35. 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

    Google Scholar 

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

    MathSciNet  Google Scholar 

  37. Wang CZ, Huang Y, Shao MW, Fan XD (2019) Fuzzy rough set-based attribute reduction using distance measures. Knowl Based Syst 164:205–212

    Google Scholar 

  38. 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

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  40. Wei W, Wu XY, Liang JY, Cui JB, Sun YJ (2018) Discernibility matrix based incremental attribute reduction for dynamic data. Knowl Based Syst 140:142–157

    Google Scholar 

  41. Wilconxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83

    Google Scholar 

  42. Wu WZ, Leung Y (2011) Theory and applications of granular labelled partitions in multi-scale decision tables. Inf Sci 181:3878–3897

    MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  44. Wu ZB, Mao KZ, Ng GW (2018) Enhanced feature fusion through irrelevant redundancy elimination in intra-class and extra-class discriminative correlation analysis. Neurocomputing 335:105–118

    Google Scholar 

  45. 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

    Google Scholar 

  46. Xu WH, Guo YT (2016) Generalized multigranulation double-quantitative decision-theoretic rough set. Knowl Based Syst 105:190–205

    Google Scholar 

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

    Google Scholar 

  48. Xu WH, Li WT, Luo SQ (2015) Knowledge reductions in generalized approximation space over two universes based on evidence theory. J Intell Fuzzy Syst 28:2471–2480

    MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  50. Yang X, Li TR, Fujita H, Liu D (2019) A sequential three-way approach to multi-class decision. Int J Approx Reason 104:108–125

    MathSciNet  MATH  Google Scholar 

  51. Yang X, Li TR, Fujita H, Liu D, Yao YY (2017) A unified model of sequential three-way decisions and multilevel incremental processing. Knowl Based Syst 134:172–188

    Google Scholar 

  52. Yang X, Li TR, Liu D, Chen HM, Luo C (2017) A unified framework of dynamic three-way probabilistic rough sets. Inf Sci 420:126–147

    MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  54. Yang XB, Song XN, Chen ZH, Yang JY (2012) On multigranulation rough sets in incomplete information system. Int J Mach Learn Cybern 3:223–232

    Google Scholar 

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

    Google Scholar 

  56. Yao YY (2018) Three-way decision and granular computing. Int J Approx Reason 103:107–123

    MATH  Google Scholar 

  57. Yao YY, Zhao Y, Wang J (2008) On reduct construction algorithms. Trans Comput Sci II(5150):100–117

    MATH  Google Scholar 

  58. Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90:111–127

    MathSciNet  MATH  Google Scholar 

  59. 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

    MATH  Google Scholar 

  60. Zhang XY, Wei L, Xu WH (2017) Attributes reduction and rules acquisition in an lattice-valued information system with fuzzy decision. Int J Mach Learn Cybern 8:135–147

    Google Scholar 

  61. Zhang Z, Xu Y, Shao L, Yang J (2018) Discriminative block-diagonal representation learning for image recognition. IEEE Trans Neural Netw Learn Syst 29:3111–3125

    MathSciNet  Google Scholar 

  62. Zhu PF, Hu QH (2013) Adaptive neighborhood granularity selection and combination based on margin distribution optimization. Inf Sci 249:1–12

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their constructive comments. In addition, the authors would like to thank Eric Appiah Mantey and Selase Tawiah Kwawu for helping us improve the language quality of this article. This work is supported by the Natural Science Foundation of China (Nos. 61572242 and 61906078) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX19_1715).

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Authors and Affiliations

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

    Keyu Liu, Xibei Yang, Hualong Yu & Xiangjian Chen

  2. Faculty of Software and Information Science, Iwate Prefectural University, Iwate, 020-0693, Japan

    Hamido Fujita

  3. School of Economics and Management, Southwest Jiaotong University, Chengdu, 610031, China

    Dun Liu

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  1. Keyu Liu
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Correspondence to Xibei Yang.

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Liu, K., Yang, X., Yu, H. et al. Supervised information granulation strategy for attribute reduction. Int. J. Mach. Learn. & Cyber. 11, 2149–2163 (2020). https://doi.org/10.1007/s13042-020-01107-5

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