Quarterly Publication

Document Type : Original Article

Author

Department of Computer Science, University of Guilan, Rasht, Iran.

10.22105/bdcv.2021.142085

Abstract

The rough sets theory is a mathematical tool to express vagueness by means of boundary region of a set. The main advantage of this implementation of vagueness is that it requires no human input or domain knowledge other than the given data set. Several efforts have been made to make close the rough sets theory and machine learning tasks. In this regard several extensions and modifications of the original theory are proposed. This paper provides the basic concepts of the theory as well as its well-known extensions and modifications.

Keywords

  1. Dubois, D., & Prade, H. (1992). Putting rough sets and fuzzy sets together. In intelligent decision support(pp. 203-232). Springer, Dordrecht.
  2. Jensen, R., & Shen, Q. (2004). Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Transactions on knowledge and data engineering16(12), 1457-1471.
  3. Jensen, R., Tuson, A., & Shen, Q. (2014). Finding rough and fuzzy-rough set reducts with SAT. Information sciences255, 100-120.
  4. Parthaláin, N., Shen, Q., & Jensen, R. (2009). A distance measure approach to exploring the rough set boundary region for attribute reduction. IEEE transactions on knowledge and data engineering22(3), 305-317.
  5. Pawlak, Z. (1982). Rough sets. International journal of computer & information sciences11(5), 341-356.
  6. Skowron, A., & Stepaniuk, J. (1996). Tolerance approximation spaces. Fundamenta informaticae27(2, 3), 245-253.
  7. Swiniarski, R. W., & Skowron, A. (2003). Rough set methods in feature selection and recognition. Pattern recognition letters24(6), 833-849.
  8. Walczak, B., & Massart, D. L. (1999). Rough sets theory. Chemometrics and intelligent laboratory systems47(1), 1-16.
  9. Ziarko, W. (1993). Variable precision rough set model. Journal of computer and system sciences46(1), 39-59.
  10. Javidi, M. M., & Eskandari, S. (2017). A noise resistant dependency measure for rough set-based feature selection. Journal of intelligent & fuzzy systems33(3), 1613-1626.