Soft Computing

, Volume 22, Issue 6, pp 1881–1889 | Cite as

A new classification method based on rough sets theory

Methodologies and Application

Abstract

Discovering the common attributes of an object is an important problem in classification. The rough sets theory (RST) successfully reveals the relationship between an object, its attributes and classes and helps bring a solution to the classification problem. In this study, a new classification method has been developed that uses RST and a similarity-based method to create the weight matrix scoring system. The proposed method is named feature weighted rough set classification (FWRSC) and is compared with the classification methods in WEKA for five different datasets. The experimental results show that FWRSC gives higher performance than most of the methods in WEKA. Additionally, FWRSC produces the highest performance in terms of accuracy with an overall average of 67.47% for five different datasets.

Keywords

Rough sets theory (RST) Data mining Classification 

Notes

Acknowledgements

This work has been partially supported by Anadolu University Scientific Research Project Commission under the Grant Number 1402F047.

Compliance with ethical standards

Conflict of interest

None.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Bouckaert RR et al (2013) WEKA manual for version 3-7-8Google Scholar
  2. Cekik R, Telceken S (2014) Classification of ECG signals using rough sets theory, Anadolu University. J Sci Technol A Appl Sci Eng 15(2):125–135Google Scholar
  3. Chakhar S, Saad I (2012) Dominance-based rough set approach for groups in multicriteria classification problems. Decis Support Syst 54(1):372–380CrossRefGoogle Scholar
  4. Cios KJ, Pedrycz W, Swiniarski RW (2012) Data mining methods for knowledge discovery, vol 458. Springer Science and Business Media, BerlinMATHGoogle Scholar
  5. Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17(2–3):191–209CrossRefMATHGoogle Scholar
  6. Duntsch T, Gediga G (1998) Uncertainty measures of rough set prediction. Artif Intell 106(1):109–137MathSciNetCrossRefMATHGoogle Scholar
  7. Garner SR (1995) Weka: the waikato environment for knowledge analysis. In: Proceedings of the New Zealand computer science research students conference, pp 57–64Google Scholar
  8. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques: concepts and techniques. Elsevier, AmsterdamMATHGoogle Scholar
  9. Hu X, Cercone N (1995) Learning in relational databases: a rough set approach. Comput Intell 11(2):323–338CrossRefGoogle Scholar
  10. Jensen R, Shen Q (2004) Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets Syst 141(3):469–485MathSciNetCrossRefMATHGoogle Scholar
  11. Khoo LP, Tor SB, Zhai LY (1999) A rough-set-based approach for classification and rule induction. Int J Adv Manuf Technol 15(6):438–444CrossRefGoogle Scholar
  12. Komorowski J, Pawlak Z, Polkowski L, Skowron A (1999) Rough sets: a tutorial. In: Pal SK, Skowron A (eds) Rough-fuzzy hybridization: a new trend in decision-making. Springer, Singapore, pp 1–98Google Scholar
  13. Kryszkiewicz M (1998) Rough set approach to incomplete information systems. Inf Sci 112(1):39–49MathSciNetCrossRefMATHGoogle Scholar
  14. Manning CD, Raghavan P, Schutze H (2008) Introduction to information retrieval, vol 1. Cambridge University Press, CambridgeCrossRefMATHGoogle Scholar
  15. Markov Z, Russell I (2006) An introduction to the WEKA data mining system. ACM SIGCSE Bull 38(3):367–368CrossRefGoogle Scholar
  16. Pawlak Z (1982) Rough sets. Int J Inf Comput Sci 11(5):341–356CrossRefMATHGoogle Scholar
  17. Pawlak Z (1984) Rough classification. Int J Man Mach Stud 20(5):469–483CrossRefMATHGoogle Scholar
  18. Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Series D: system theory, knowledge engineering and problem solving, vol 9. Kluwer, DordrechtGoogle Scholar
  19. Pawlak Z (1998) Rough set theory and its applications to data analysis. Cybern Syst 29(7):661–688CrossRefMATHGoogle Scholar
  20. Pawlak Z (2002) Rough sets and intelligent data analysis. Inf Sci 147(1):1–12MathSciNetCrossRefMATHGoogle Scholar
  21. Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci 177(1):3–27MathSciNetCrossRefMATHGoogle Scholar
  22. Pawlak Z, Sowinski R (1994) Rough set approach to multi-attribute decision analysis. Eur J Oper Res 72(3):443–459CrossRefMATHGoogle Scholar
  23. Slowinski R, Zopounidis C (1995) Application of the rough set approach to evaluation of bankruptcy risk. Intell Syst Account Finance Manag 4(1):27–41CrossRefGoogle Scholar
  24. Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recognit Lett 24(6):833–849CrossRefMATHGoogle Scholar
  25. Tan PN, Steinbach M, Kumar V (2006) Classification: basic concepts, decision trees, and model evaluation. Introduct Data Min 1:145–205Google Scholar
  26. Witten IH, Frank E (2005) Data mining, practical machine learning tools and techniques. Morgan Kaufmann, Los AltosMATHGoogle Scholar
  27. Xiang-Wei L, Yian-Fang Q (2012) A data preprocessing algorithm for classification model based on Rough sets. Phys Proc 25:2025–2029CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer EngineeringAnadolu UniversityEskisehirTurkey

Personalised recommendations