A rule-extraction framework under multigranulation rough sets

作者：Liu Xin^*; Qian Yuhua; Liang Jiye

来源：International Journal of Machine Learning and Cybernetics, 2014, 5(2): 319-326.

DOI：10.1007/s13042-013-0194-0

• Multigranulation rough set
• Rule extraction
• Granulation selection
• Granule selection

The multigranulation rough set (MGRS) is becoming a rising theory in rough set area, which offers a desirable theoretical method for problem solving under multigranulation environment. However, it is worth noticing that how to effectively extract decision rules in terms of multigranulation rough sets has not been more concerned. In order to address this issue, we firstly give a general rule-extraction framework through including granulation selection and granule selection in the context of MGRS. Then, two methods in the framework (i.e. a granulation selection method that employs a heuristic strategy for searching a minimal set of granular structures and a granule selection method constructed by an optimistic strategy for getting a set of granules with maximal covering property) are both presented. Finally, an experimental analysis shows the validity of the proposed rule-extraction framework in this paper.

• 出版日期2014-4
• 单位山西大学