Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Attributes reduction and rules acquisition in an lattice-valued information system with fuzzy decision

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

Abstract

In this paper, we aim to investigate lattice-valued information systems with fuzzy decision (LvISFD), where the domain of every condition attribute is a finite lattice. Firstly, we propose the concept of LvISFD by combining dominance relation and lattice structure. Meanwhile, we establish a rough set approach and give a ranking method for all objects in this complex system. Secondly, we address approximation reductions and rules acquisition in LvISFD. Furthermore, an algorithm of the presented reduction approach is constructed. Finally, an illustrative example is given to show the effectiveness of the proposed method, and experiment evaluation is performed by four datasets from UCI. These results of this study will be more valuable to solve practical issues.

This is a preview of subscription content, log in via an institution to check access.

Access this article

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.

Similar content being viewed by others

Explore related subjects

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

References

  1. Dembczynski K, Pindur R, Susmaga R (2003) Generation of exhaustive set of rules within dominance-based rough set approach. Electron Notes Theory Comput Sci 82:96–107

    Article  MATH  Google Scholar 

  2. Dembczynski K, Pindur R, Susmaga R (2003) Dominance-based rough set classifier without induction of decision rules. Electron Notes Theory Comput Sci 82:84–95

    Article  MATH  Google Scholar 

  3. Fan BJ, Xu WH, Yu JH (2014) Uncertainty measures in ordered information system based on approximation operators. Abstr Appl Anal 2014:1–17

    MathSciNet  Google Scholar 

  4. Greco S, Matarazzo B, Slowinski R (1999) Rough approximation of a preference relation by dominance relatioin, ICS Research Report 16/96, Warsaw University of Technology; 1996 and in Europe. J Oper Res 117:63–83

    Article  Google Scholar 

  5. Greco S, Matarazzo B, Slowinski R (2001) Rough set theory for multicriteria decision analysis. Eur J Oper Res 129:11–47

    Article  MATH  Google Scholar 

  6. Greco S, Matarazzo B, Slowinski R (2007) Dominance-based rough set approach as a proper way of handling graduality in rough set theory. Trans Rough Sets VII Lect Notes Comput Sci 4400:36–52

    Article  MathSciNet  MATH  Google Scholar 

  7. Kryszkiewicz M (2001) Comparative study of alternative type of attribute reduction ininconsistent systems. Int J Intell Syst 16:105–120

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  9. Leung Y, Wu WZ, Zhang WX (2006) Knowledge acquisition in incomplete information systems: a rough set approach. Eur J Oper Res 168:164–180

    Article  MathSciNet  MATH  Google Scholar 

  10. Li WT, Xu WH (2015) Multigranulation decision-theoretic rough set in ordered information system. Fundam Inform 139:67–89

    Article  MathSciNet  MATH  Google Scholar 

  11. Li WT, Xu WH (2014) Probabilistic rough set model based on dominance relation. In: Proceedings of rough sets and knowledge technology, lecture notes in artificial intelligence, vol 8818, pp 856–863

  12. Nguyen HS, Slezak D (1999) Approximation reducts and association rules correspondence and complexity results. In: Proceedings of RSFDGrC’99, Yamaguchi, Japan, LNAI 1711, pp 137–145

  13. Pan HY, Cao YZ, Zhang M, Chen YX (2014) Simulation for lattice-valued doubly labeled transition systems. Int J Approx Reason 55:797–811

    Article  MathSciNet  MATH  Google Scholar 

  14. Pan HY, Li YM, Cao YZ (2015) Lattice-valued simulations for quantitative transition systems. Int J Approx Reason 56:28–42

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  16. Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci 177:3–27

    Article  MathSciNet  MATH  Google Scholar 

  17. Pawlak Z, Skowron A (2007) Rough sets: some extensions. Inf Sci 177:28–40

    Article  MathSciNet  MATH  Google Scholar 

  18. Quafatou M (2000) A generalization of rough set theory. Inf Sci 124:301–316

    Article  MathSciNet  Google Scholar 

  19. Shao MW, Zhang WX (2005) Dominance relation and relus in an incomplete ordered information system. Int J Intell Syst 20:13–27

    Article  MATH  Google Scholar 

  20. Slowinski R, Zopounidis C, Dimitras AI (1997) Prediction of company acquisition in Greece by means of the rough set approach. Eur J Oper Res 100:1–15

    Article  MATH  Google Scholar 

  21. Stefanowski J (1998) On rough set based approaches to induction of decision rules. Rough Sets Knowl Discov 1:500–529

    MATH  Google Scholar 

  22. Susmaga R, Slowinski R, Greco S, Matarazzo B (2000) Generation of reducts and rules in multi-attribute and multi-criteria classification. Control Cybern 4:969–988

    MATH  Google Scholar 

  23. Thangavel K, Pethalakshmi A (2009) Dimensionality reduction based on rough set theory: a review. Appl Soft Comput 9:1–12

    Article  Google Scholar 

  24. Wu QX, Bell D, McGinnity M (2005) Multiknowledge for decision making. Knowl Inf Syst 7:246–266

    Article  Google Scholar 

  25. Wu WZ, Leung Y, Zhang WX (2002) Connections between rough set theory and Dempster–Shafer theory of evidence. Int J Gen Syst 31:405–430

    Article  MathSciNet  MATH  Google Scholar 

  26. Wu WZ, Zhang M, Li HZ, Mi JS (2005) Attribute reduction in random information systems via Dempster–Shafer theory of evidence. Inf Sci 174:143–164

    Article  MATH  Google Scholar 

  27. Wang R, Kwon S, Wang XZ, Jiang QS (2015) Segment based decision tree induction with continuous valued attributes. IEEE Trans Cybern 45:1262–1275

    Article  Google Scholar 

  28. Wang XZ, Xing HJ, Li Yan et al (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23:1638–1654

    Article  Google Scholar 

  29. Wang XZ (2015) Uncertainty in learning from big data-editorial. J Intell Fuzzy Syst 28:2329–2330

    Article  Google Scholar 

  30. Wang XZ, Hong JR (1998) On the handling of fuzziness for continuous-valued attributes in decision tree generation. Fuzzy Sets Syst 99:283–290

    Article  MathSciNet  MATH  Google Scholar 

  31. Wang XZ, Aamir R, Fu AM (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29:1185C1196

    MathSciNet  Google Scholar 

  32. Xu WH, Li Y, Liao XW (2012) Approaches to attribute reductions based on rough set and matrix computation in inconsistent ordered information systems. Knowl-Based Syst 27:78–91

    Article  Google Scholar 

  33. Xu WH, Liu SH, Yu FS (2013) Knowledge reduction in lattice-valued information systems with interval-valued intuitionistic fuzzy decision. Int J Artif Intell Tools 22:1–29

    Google Scholar 

  34. Xu WH, Zhang WX (2007) Measuring roughness of generalized rough sets induced by a covering. Fuzzy Sets Syst 158:2443–2455

    Article  MathSciNet  MATH  Google Scholar 

  35. Xu WH, Zhang XY, Zhong JM, Zhang WX (2010) Attribute reduction in ordered information systems based on evidence theory. Knowl Inf Syst 25:169–184

    Article  Google Scholar 

  36. Yu DR, Hu QH, Wu CX (2007) Uncertainty measures for fuzzy relations and their applications. Appl Soft Comput 7:1135–1143

    Article  Google Scholar 

  37. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MathSciNet  MATH  Google Scholar 

  38. Zhang XY, Xu WH (2011) Rough set approach to approximation reduction in ordered decision table with fuzzy decision. Math Probl Eng 2011:1–16

    MathSciNet  MATH  Google Scholar 

  39. Zhang XY, Xu WH (2012) Ranking for objects and attribute reductions in intuitionistic fuzzy ordered information systems. Math Probl Eng 2012:1–19

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

The author would like to thank the valuable suggestions from the anonymous referees and the editor in chief for improving the quality of the paper. This work is supported by Natural Science Foundation of China (Nos. 61105041, 11371014, 61472463, 61402064), National Natural Science Foundation of CQ CSTC (Nos. cstc2013jcyjA40051, cstc2015jcyjA40053), Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education (No. 30920140122006), Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province (No. OBDMA201503).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ling Wei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Wei, L. & Xu, W. Attributes reduction and rules acquisition in an lattice-valued information system with fuzzy decision. Int. J. Mach. Learn. & Cyber. 8, 135–147 (2017). https://doi.org/10.1007/s13042-015-0492-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-015-0492-9

Keywords