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A variable precision grey-based multi-granulation rough set model and attribute reduction
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DOI：10.1016/j.knosys.2018.02.033.png)
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Abstract
En 中文
Exploring rough set theory in the viewpoint of multi-granulation gradually attracts scholars attention in recent years. To handle uncertainty problems with grey information, in this paper, we devise a variable precision grey multi-granulation rough set (VPG-MGRS) by combining with grey system theory and multi granulation rough set. We utilize the grey relational relation for further establishing multiple granular structures and then adopt a threshold to control the number of condition satisfied. After discussing several important properties of VPG-MGRS, we discover that the proposed VPG-MGRS model is a generalized classical MGRS. Meanwhile, we redefine the significance measures of attribute based on VPG-MGRS for attribute reduction. Last but not least, theoretical studies and numerical experiments have demonstrated that the VPG-MRGS-based attribute reduction algorithm is of feasibility and effectivity in handling uncertainty problems with grey information and provides a new technique for knowledge discovery, and the VPG-MGRS model enlarges the application fields of MGRS. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Variable precision
Grey system theory
Grey relational class
Multi-granulation rough set
Attribute reduction
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