Skip to main content
Springer Nature Link
Log in

On selection of optimal cuts in complete multi-scale decision tables

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

In this paper, a novel optimal scale selection method in complete multi-scale decision tables has been proposed. Unlike the existing approaches in the literature, we employ the tools of granularity trees and cuts for each attribute. Each granularity tree has many different local cuts, which represent various scale selection methods under a specific attribute. Different local cuts collectively forms a global cut of a multi-scale information table, which in turn induces an information table with a mixed scale. One distinct feature of such tables is that the attribute values of different objects may be obtained at different scales for the same attribute. By keeping maximal consistency of the derived mixed-scale decision table, we introduce the notions of optimal cuts in multi-scale decision tables. Then, a comparative study between different types of optimal scale selection methods is performed. Finally, an algorithm is designed to verify the validity of the proposed approach.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

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

References

  • Bělohlavek R, De Baets B, Konecny J (2014) Granularity of attributes in formal concept analysis. Inf Sci 260:149–170

    Article  MathSciNet  Google Scholar 

  • Chen HM, Li TR, Qiao SJ, Ruan D (2010) A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values. Int J Intell Syst 25(10):1005–1026

    Article  Google Scholar 

  • Feng QR, Miao DQ, Cheng Y (2010) Hierarchical decision rules mining. Expert Syst Appl 37:2081–2091

    Article  Google Scholar 

  • Gu SM, Wu WZ (2013) On knowledge acquisition in multi-scale decision systems. Int J Mach Learn Cybern 4:477–486

    Article  Google Scholar 

  • Gu SM, Wan YH, Wu WZ et al (2016) Local optimal granularity selections in multi-granular decision systems. J Nanjing Univ (Nat Sci) 52(2):280–288 (in Chinese)

    MATH  Google Scholar 

  • Hao C, Li JH, Fan M, Li WQ, Tsang ECC (2017) Optimal scale selection in dynamic multi-scale decision tables based on sequential three-way decisions. Inf Sci 415–416(2017):213–232

    Article  Google Scholar 

  • Hong TP, Liou YL, Wang SL (2009) Fuzzy rough sets with hierarchical quantitative attributes. Expert Syst Appl 36:6790–6799

    Article  Google Scholar 

  • Luo C, Li TR, Chen HM et al (2018) Incremental rough set approach for hierarchical multicriteria classification. Inf Sci 429:72–87

    Article  MathSciNet  Google Scholar 

  • Luo C, Li TR, Huang YY et al (2019) Updating three-way decisions in incomplete multi-scale information systems. Inf Sci 476:274–289

    Article  Google Scholar 

  • Li F, Hu BQ (2017) A new approach of optimal scale selection to multi-scale decision tables. Inf Sci 381:193–208

    Article  Google Scholar 

  • Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356

    Article  Google Scholar 

  • Qian J, Lv P, Yue XD et al (2015) Hierarchical attribute reduction algorithms for big data using MapReduce. Knowl Based Syst 73:18–31

    Article  Google Scholar 

  • Shao MW, Lv MM, Li KW et al (2019) The construction of attribute (object)-oriented multi-granularity concept lattices. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-019-00955-0

    Article  Google Scholar 

  • She YH, Li JH, Yang HL (2015) A local approach to rule induction in multi-scale decision tables. Knowl Based Syst 89:398–410

    Article  Google Scholar 

  • Wan Y, Zou LG (2019) An efficient algorithm for decreasing the granularity levels of attributes in formal concept analysis. IEEE Access 7:11029–11040

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wu WZ, Leung Y (2013) Optimal scale selection for multi-scale decision tables. Int J Approx Reason 54:1107–1129

    Article  MathSciNet  Google Scholar 

  • Wu WZ, Leung Y (2019) A comparison study of optimal scale combination selection in generalized multi-scale decision tables. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-019-00954-1

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Ye MQ, Wu XD, Hu XG, Hu DH (2013) Anonymizing classification data using rough set theory. Knowl Based Syst 43:82–94

    Article  Google Scholar 

  • Ye MQ, Wu XD, Hu XG, Hu DH (2013) Multi-level rough set reduction for decision rule mining. Appl Intell 39:642–658

    Article  Google Scholar 

  • Ye MQ, Wu XD, Hu XG, Hu DH (2014) Knowledge reduction for decision tables with attribute value taxonomies. Knowl Based Syst 56:68–78

    Article  Google Scholar 

  • Zhang J, Honavar V (2003) Learning decision tree classifiers from attribute value taxonomies and partially specified data. In: Proceedings of the 19th international conference on machine learning, pp 880–887

  • Zou LG, Zhang ZP, Long J (2016) An efficient algorithm for increasing the granularity levels of attributes in formal concept analysis. Expert Syst Appl 46:224–235

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the National Nature Science Foundation of China (Nos. 61976244, 12001422, 61472471 and 11531009).

Author information

Authors and Affiliations

  1. College of Science, Xi’an Shiyou University, Xi’an, 710065, China

    Yanhong She, Wenli Zheng & Xiaoli He

  2. College of Computer Science, Xi’an Shiyou University, Xi’an, 710065, China

    Zhuojun Zhao & Mengting Hu

Authors
  1. Yanhong She
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhuojun Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mengting Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Wenli Zheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xiaoli He
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yanhong She.

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

She, Y., Zhao, Z., Hu, M. et al. On selection of optimal cuts in complete multi-scale decision tables. Artif Intell Rev 54, 6125–6148 (2021). https://doi.org/10.1007/s10462-021-09965-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-021-09965-3

Keywords