Rule Acquisition Algorithm for Neighborhood Multi-granularity Rough Sets Based on Maximal Granule
CHEN Jingwen1, MA Fumin1, ZHANG Tengfei2, ZENG Yonggang1
1.College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023 2.School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023
Abstract：Granular computing based rule acquisition algorithms remedy the defects of rule acquisition algorithms to some extent. However, most of these algorithms can merely deal with categorical data. To further process the numerical or mixed data from the perspective of multi-granularity and multi-level, the neighborhood multi-granularity rough set model is adopted. Through calculating neighborhood multi-granularity condition granules and decision granules, the redundancy relation of condition granules in the process of rule acquisition is analyzed, and thus the redundant condition granules are further pruned. A rule acquisition algorithm for neighborhood multi-granularity rough set based on maximal granule is developed. The validity and superiority of the proposed algorithm are demonstrated by theoretical analysis and comparable experiments.
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2017, Vol. 30 
