Variable Precision Rough Set Model for Incomplete Information Systems and Its Beta-Reducts

Authors

  • Zengtai Gong College of Mathematics and Statistics, Northwest Normal University, Lanzhou
  • Zhanhong Shi College of Mathematics and Statistics, Northwest Normal University, Lanzhou
  • Hongxia Yao College of Mathematics and Statistics, Northwest Normal University, Lanzhou

Keywords:

Variable precision rough sets, incomplete information systems, approximation space, tolerance relation, beta-reducts

Abstract

As the original rough set model is quite sensitive to noisy data, Ziarko proposed the variable precision rough set (VPRS) model to deal with noisy data and uncertain information. This model allowed for some degree of uncertainty and misclassification in the mining process. In this paper, the variable precision rough set model for an incomplete information system is proposed by combining the VPRS model and incomplete information system, and the beta-lower and beta-upper approximations are defined. Considering that classical VPRS model lacks a feasible method to determine the precision parameter beta when calculating the beta-reducts, we present an approach to determine the parameter beta. Then, by calculating discernibility matrix and discernibility functions based on beta-lower approximation, the beta-reducts and the generalized decision rules are obtained. Finally, a concrete example is given to explain the validity and practicability of beta-reducts which is proposed in this paper.

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Published

2013-01-30

How to Cite

Gong, Z., Shi, Z., & Yao, H. (2013). Variable Precision Rough Set Model for Incomplete Information Systems and Its Beta-Reducts. Computing and Informatics, 31(6+), 1385–1399. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/1323