Skip to content
Research Article Open access CC BY 3.0

Attributes Reduction Based on Interval-Valued Fuzzy Rough Sets

Zhiming Zhang

Journal of Advances in Mathematics and Computer Science · pp. 2046–2066 · Published 27 May 2014

10.9734/BJMCS/2014/10426

Abstract

Aims: This paper provides a systematic study on attribute reduction with interval-valued fuzzy rough sets. Study Design: The interval-valued fuzzy rough sets are an important improvement of traditional rough set model to deal with both fuzziness and vagueness in data which the traditional one cannot handle. Place and Duration of Study: The existing researches on interval-valued fuzzy rough sets mainly focus on the establishment of lower and upper approximation operators by using constructive and axiomatic approaches. Less effort has been put on the attributes reduction of databases based on interval-valued fuzzy rough sets. Methodology: After introducing some concepts and theorems of attributes reduction with interval-valued fuzzy rough sets, we study the structure of the attributes reduction with interval-valued fuzzy rough sets and present an algorithm by using discernibility matrix to find all the attributes reductions with interval-valued fuzzy rough sets. Results: Finally, we propose an example to demonstrate our idea and method in this paper. Conclusion: With these discussions we construct a basic foundation for attributes reduction based on interval-valued fuzzy rough sets.

Interval-valued fuzzy rough sets rough sets attributes reduction discernibility matrix

References (40)

  1. 1 Fuzzy sets
  2. 2 The concept of a linguistic variable and its application to approximate reasoning-III [DOI]
  3. 3 Rough Sets: Theoretical Aspects of Reasoning about Data
  4. 4 ROUGH FUZZY SETS AND FUZZY ROUGH SETS* [DOI]
  5. 5 The Discernibility Matrices and Functions in Information Systems [DOI]
  6. 6 Rough set approach to incomplete information systems [DOI]
  7. 7 Interval valued fuzzy sets based on normal forms [DOI]
  8. 8 A method of inference in approximate reasoning based on interval-valued fuzzy sets [DOI]
  9. 9 Relational interpretations of neighborhood operators and rough set approximation operators [DOI]
  10. 10 Rough Sets in Knowledge Discovery 2 [DOI]
  11. 11 Intelligent decision support. Handbook of applications and advances of the rough sets theory [DOI]
  12. 12 Reduction and axiomization of covering generalized rough sets [DOI]
  13. 13 Attribute reduction in decision-theoretic rough set models [DOI]
  14. 14 Rough Sets, Fuzzy Sets and Knowledge Discovery [DOI]
  15. 15 Fuzzy-Rough Sets Assisted Attribute Selection [DOI]
  16. 16 Information-preserving hybrid data reduction based on fuzzy-rough techniques [DOI]
  17. 17 On Three Types of Covering-Based Rough Sets [DOI]
  18. 18 AN APPROACH FOR COMBINING LINGUISTIC AND NUMERICAL INFORMATION BASED ON THE 2-TUPLE FUZZY LINGUISTIC REPRESENTATION MODEL IN DECISION-MAKING [DOI]
  19. 19 Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation [DOI]
  20. 20 Attributes Reduction Using Fuzzy Rough Sets [DOI]
  21. 21 A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets [DOI]
  22. 22 Knowledge acquisition in incomplete information systems: A rough set approach [DOI]
  23. 23 Automated extraction of medical expert system rules from clinical databases based on rough set theory [DOI]
  24. 24 Indicator of inclusion grade for interval-valued fuzzy sets. Application to approximate reasoning based on interval-valued fuzzy sets [DOI]
  25. 25 Interval-valued fuzzy sets constructed from matrices: Application to edge detection [DOI]
  26. 26 Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model [DOI]
  27. 27 Fuzzy risk analysis based on interval-valued fuzzy numbers [DOI]
  28. 28 Fuzzy rough set theory for the interval-valued fuzzy information systems [DOI]
  29. 29 Approximations and reducts with covering generalized rough sets [DOI]
  30. 30 A systematic study on attribute reduction with rough sets based on general binary relations [DOI]
  31. 31 On characterization of generalized interval-valued fuzzy rough sets on two universes of discourse [DOI]
  32. 32 An approximate analogical reasoning schema based on similarity measures and interval-valued fuzzy sets [DOI]
  33. 33 An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems [DOI]
  34. 34 An interval-valued fuzzy inference method— Some basic properties [DOI]
  35. 35 Knowledge acquisition in incomplete fuzzy information systems via the rough set approach [DOI]
  36. 36 Development of an interval-valued fuzzy linear-programming method based on infinite α-cuts for water resources management [DOI]
  37. 37 On fuzzy approximation operators in attribute reduction with fuzzy rough sets [DOI]
  38. 38 Level sets and the extension principle for interval valued fuzzy sets and its application to uncertainty measures [DOI]
  39. 39 Evaluating students’ answerscripts based on interval-valued fuzzy grade sheets [DOI]
  40. 40 Interval-valued fuzzy relation-based clustering with its application to performance evaluation [DOI]

Cited by 0

No indexed citations yet.

Related research

Article metrics

Real usage data collected on this platform.

56

Page views

0

PDF downloads

19

Outbound clicks

0

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.