Skip to main content
SpringerLink
Log in

Knowledge granularity based incremental attribute reduction for incomplete decision systems

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Attribute reduction is an important application of rough set theory. With the dynamic changes of data becoming more and more common, traditional attribute reduction, also called static attribute reduction, is no longer efficient. How to update attribute reducts efficiently gets more and more attention. In the light of the variation about the number of objects, we focus on incremental attribute reduction approaches based on knowledge granularity which can be used to measure the uncertainty in incomplete decision systems. We first introduce incremental mechanisms to calculate knowledge granularity for incomplete decision systems when multiple objects vary dynamically. Then, incremental attribute reduction algorithms for incomplete decision systems when adding multiple objects and when deleting multiple objects are proposed respectively. Finally, comparative experiments on different real-life data sets are conducted to demonstrate the effectiveness and efficiency of the proposed incremental algorithms for updating attribute reducts with the variation of multiple objects in incomplete decision systems.

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
Fig. 2

Similar content being viewed by others

References

  1. Pawlak Z (1991) Rough sets: theoretical aspect of reasoning about data. Kluwer Academic Publishers, Dordrecht

    MATH  Google Scholar 

  2. Wang R, Wang X, Kwong S, Xu C (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEE Trans Fuzzy Syst 25(6):1460–1475

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  4. Wang X, Xing H, Li Y, Hua Q, Dong C, Pedrycz W (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654

    Google Scholar 

  5. Dai J, Tian H, Wang W, Liu L (2013) Decision rule mining using classification consistency rate. Knowl-Based Syst 43:95–102

    Google Scholar 

  6. Zhao B, Ren Y, Gao D (2019) Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy bandelet neural network. Appl Soft Comput 78:132–140

    Google Scholar 

  7. Hao C, Li J, Fan M, Liu W, Tsang ECC (2017) Optimal scale selection in dynamic multi-scale decision tables based on sequential three-way decisions. Inf Sci 415:213–232

    Google Scholar 

  8. Liang D, Xu Z, Liu D (2017) Three-way decisions based on decision-theoretic rough sets with dual hesitant fuzzy information. Inf Sci 396:127–143

    MATH  Google Scholar 

  9. Wang X, Zhai J, Lu S (2008) Induction of multiple fuzzy decision trees based on rough set technique. Inf Sci 178(16):3188–3202

    MathSciNet  MATH  Google Scholar 

  10. Liu X, Qian Y, Liang J (2014) A rule-extraction framework under multigranulation rough sets. Int J Mach Learn Cybern 5(2):319–326

    Google Scholar 

  11. Zhang X, Mei C, Chen D, Li J (2014) Multi-confidence rule acquisition and confidence-preserved attribute reduction in interval-valued decision systems. Int J Approx Reason 55(8):1787–1804

    MathSciNet  MATH  Google Scholar 

  12. Cheruku R, Edla DR, Kuppili V, Dharavath R (2018) RST-BatMiner: a fuzzy rule miner integrating rough set feature selection and bat optimization for detection of diabetes disease. Appl Soft Comput 67:764–780

    Google Scholar 

  13. Hamouda SKM, Wahed ME, Alez RHA, Riad K (2018) Robust breast cancer prediction system based on rough set theory at National Cancer Institute of Egypt. Comput Methods Programs Biomed 153:259–268

    Google Scholar 

  14. Jothi G, Inbarani HH (2016) Hybrid tolerance rough set-firefly based supervised feature selection for MRI brain tumor image classification. Appl Soft Comput 46:639–651

    Google Scholar 

  15. Dai J, Xu Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput 13(1):211–221

    Google Scholar 

  16. Sun L, Zhang X, Qian Y, Xu J, Zhang S, Tian Y (2019) Joint neighborhood entropy-based gene selection method with fisher score for tumor classification. Appl Intell 49(4):1245–1259

    Google Scholar 

  17. Dai J, Wang W, Xu Q, Tian H (2012) Uncertainty measurement for interval-valued decision systems based on extended conditional entropy. Knowl Based Syst 27:443–450

    Google Scholar 

  18. Dai J, Wang W, Mi J (2013) Uncertainty measurement for interval-valued information systems. Inf Sci 251:63–78

    MathSciNet  MATH  Google Scholar 

  19. Dai J, Hu H, Hu Q, Huang W, Zheng N, Liu L (2018) Locally linear approximation approach for incomplete data. IEEE Trans Cybern 48(6):1720–1732

    Google Scholar 

  20. Wang C, Huang Y, Shao M, Chen D (2019) Uncertainty measures for general fuzzy relations. Fuzzy Sets Syst 360:82–96

    MathSciNet  MATH  Google Scholar 

  21. Dai J, Wei B, Zhang X, Zhang Q (2017b) Uncertainty measurement for incomplete interval-valued information systems based on \(\alpha\)-weak similarity. Knowl Based Syst 136:159–171

    Google Scholar 

  22. Ko YC, Fujita H, Li T (2017) An evidential analysis of Altman Z-score for financial predictions: case study on solar energy companies. Appl Soft Comput 52:748–759

    Google Scholar 

  23. Lei L (2018) Wavelet neural network prediction method of stock price trend based on rough set attribute reduction. Appl Soft Comput 62:923–932

    Google Scholar 

  24. Singh AK, Baranwal N, Nandi GC (2019) A rough set based reasoning approach for criminal identification. Int J Mach Learn Cybern 10(3):413–431

    Google Scholar 

  25. Wang X, Wang R, Xu C (2018b) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern 48(2):703–715

    MathSciNet  Google Scholar 

  26. Fan J, Jiang Y, Liu Y (2017) Quick attribute reduction with generalized indiscernibility models. Inf Sci 397:15–36

    Google Scholar 

  27. Ni P, Zhao S, Wang X, Chen H, Li C (2019) PARA: a positive-region based attribute reduction accelerator. Inf Sci 503:533–550

    Google Scholar 

  28. Dai J, Tian H (2013) Fuzzy rough set model for set-valued data. Fuzzy Sets Syst 229:54–68

    MathSciNet  MATH  Google Scholar 

  29. Dai J (2013) Rough set approach to incomplete numerical data. Inf Sci 240:43–57

    MathSciNet  MATH  Google Scholar 

  30. Konecny J, Krajca P (2018) On attribute reduction in concept lattices: experimental evaluation shows discernibility matrix based methods inefficient. Inf Sci 467:431–445

    Google Scholar 

  31. Wang C, He Q, Shao M, Hu Q (2018a) Feature selection based on maximal neighborhood discernibility. Int J Mach Learn Cybern 9(11):1929–1940

    Google Scholar 

  32. Dai J, Hu Q, Zhang J, Hu H, Zheng N (2017) Attribute selection for partially labeled categorical data by rough set approach. IEEE Trans Cybern 47(9):2460–2471

    Google Scholar 

  33. Dai J, Tian H (2013) Entropy measures and granularity measures for set-valued information systems. Inf Sci 240(11):72–82

    MathSciNet  MATH  Google Scholar 

  34. Wang F, Liang J, Dang C (2013) Attribute reduction for dynamic data sets. Appl Soft Comput 13(1):676–689

    Google Scholar 

  35. Bhattacharya A, Goswami RT, Mukherjee K (2019) A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of android malwares. Int J Mach Learn Cybern 10(7):1893–1907

    Google Scholar 

  36. Dai J, Hu Q, Hu H, Huang D (2018a) Neighbor inconsistent pair selection for attribute reduction by rough set approach. IEEE Trans Fuzzy Syst 26:937–950

    Google Scholar 

  37. Dai J, Hu H, Wu WZ, Qian Y, Huang D (2018) Maximal discernibility pairs based approach to attribute reduction in fuzzy rough sets. IEEE Trans Fuzzy Syst 26(4):2174–2187

    Google Scholar 

  38. Li F, Jin C, Yang J (2019) Roughness measure based on description ability for attribute reduction in information system. Int J Mach Learn Cybern 10(5):925–934

    Google Scholar 

  39. Liu K, Yang X, Yu H, Mi J, Wang P, Chen X (2019) Rough set based semi-supervised feature selection via ensemble selector. Knowl Based Syst 165:282–296

    Google Scholar 

  40. Wang C, Huang Y, Shao M, Fan X (2019) Fuzzy rough set-based attribute reduction using distance measures. Knowl Based Syst 164:205–212

    Google Scholar 

  41. Dai J, Han H, Hu Q, Liu M (2016) Discrete particle swarm optimization approach for cost sensitive attribute reduction. Knowl Based Syst 102:116–126

    Google Scholar 

  42. Li Y, Jin Y, Sun X (2018) Incremental method of updating approximations in DRSA under variations of multiple objects. Int J Mach Learn Cybern 9(2):295–308

    Google Scholar 

  43. Liang J, Wang F, Dang C, Qian Y (2014) A group incremental approach to feature selection applying rough set technique. IEEE Trans Knowl Data Eng 26(2):294–308

    Google Scholar 

  44. Ma F, Ding M, Zhang T (2019) Compressed binary discernibility matrix based incremental attribute reduction algorithm for group dynamic data. Neurocomputing 294:20–27

    Google Scholar 

  45. Wei W, Song P, Liang J, Wu X (2019) Accelerating incremental attribute reduction algorithm by compacting a decision table. Int J Mach Learn Cybern 10(9):2355–2373

    Google Scholar 

  46. Yang Y, Chen D, Wang H (2017) Active sample selection based incremental algorithm for attribute reduction with rough sets. IEEE Trans Fuzzy Syst 25(4):825–838

    Google Scholar 

  47. Yang Y, Chen D, Wang H (2018) Incremental perspective for feature selection based on fuzzy rough sets. IEEE Trans Fuzzy Syst 26(3):1257–1273

    Google Scholar 

  48. Jing Y, Li T, Huang J, Zhang Y (2016a) An incremental attribute reduction approach based on knowledge granularity under the attribute generalization. Int J Approx Reason 76:80–95

    MathSciNet  MATH  Google Scholar 

  49. Jing Y, Li T, Huang J, Chen H, Horng SJ (2017) A group incremental reduction algorithm with varying data values. Int J Intell Syst 32(9):900–925

    Google Scholar 

  50. Wei W, Wu X, Liang J, Cui J, Sun Y (2018) Discernibility matrix based incremental attribute reduction for dynamic data. Knowl Based Syst 140:142–157

    Google Scholar 

  51. Jing Y, Li T, Fujita H, Wang B, Cheng N (2018) An incremental attribute reduction method for dynamic data mining. Inf Sci 465:202–218

    MathSciNet  Google Scholar 

  52. Yang C, Ge H, Li L, Ding J (2019) A unified incremental reduction with the variations of the object for decision tables. Soft Comput 23(15):6407–6427

    MATH  Google Scholar 

  53. Shu W, Shen H (2014a) Incremental feature selection based on rough set in dynamic incomplete data. Pattern Recogn 47(12):3890–3906

    Google Scholar 

  54. Shu W, Shen H (2014b) Updating attribute reduction in incomplete decision systems with the variation of attribute set. Int J Approx Reason 55(3):867–884

    MathSciNet  MATH  Google Scholar 

  55. Xie X, Qin X (2018) A novel incremental attribute reduction approach for dynamic incomplete decision systems. Int J Approx Reason 93:443–462

    MathSciNet  MATH  Google Scholar 

  56. Luo C, Li T, Yao Y (2017) Dynamic probabilistic rough sets with incomplete data. Inf Sci 417:39–54

    Google Scholar 

  57. Zhang C, Dai J (2019) An incremental attribute reduction approach based on knowledge granularity for incomplete decision systems. Granul Comput. https://doi.org/10.1007/s41066-019-00173-7

    Article  Google Scholar 

  58. Kryszkiewicz M (1998) Rough set approach to incomplete information systems. Inf Sci 112(1):39–49

    MathSciNet  MATH  Google Scholar 

  59. Jing Y, Li T, Luo C, Horng SJ, Wang G, Yu Z (2016b) An incremental approach for attribute reduction based on knowledge granularity. Knowl Based Syst 104:24–38

    Google Scholar 

Download references

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Nos. 61976089, 61473259, 61070074, and 60703038), and the Hunan Provincial Science and Technology Project Foundation (2018TP1018, 2018RS3065).

Author information

Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, China

    Chucai Zhang, Jianhua Dai & Jiaolong Chen

Authors
  1. Chucai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianhua Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiaolong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianhua Dai.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., Dai, J. & Chen, J. Knowledge granularity based incremental attribute reduction for incomplete decision systems. Int. J. Mach. Learn. & Cyber. 11, 1141–1157 (2020). https://doi.org/10.1007/s13042-020-01089-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-020-01089-4

Keywords

Search

Navigation