期刊
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
卷 5, 期 6, 页码 981-990出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-014-0242-4
关键词
Granular structure; Granular structure selection; Lower approximation; Multigranulation fuzzy rough set; Upper approximation
资金
- Natural Science Foundation of China [61100116, 61272419]
- Natural Science Foundation of Jiangsu Province of China [BK2011492, BK2012700, BK20130471]
- Natural Science Foundation of Jiangsu Higher Education Institutions of China [13KJB520003]
- Qing Lan Project of Jiangsu Province of China
- Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education [30920130122005]
- Postgraduate Innovation Foundation of University in Jiangsu Province of China [CXLX13_707]
- Foundation of Artificial Intelligence of Key Laboratory of Sichuan Province [2013RYJ03]
As we all known, dynamic updating of rough approximations and reducts are keys to the applications of the rough set theory in real data sets. In recent years, with respect to different requirements, many approaches have been proposed to study such problems. Nevertheless, few of the them are carried out under multigranulation fuzzy environment. To fill such gap, the updating computations of multigranulation fuzzy rough approximations are explored in this paper. By considering the dynamic increasing of fuzzy granular structures, which are induced by fuzzy relations, naive and fast algorithms are presented, respectively. Moreover, both naive and fast forward greedy algorithms are designed for granular structure selection in dynamic updating environment. Experiments on six data sets from UCI show that fast algorithms are more effective for reducing computational time in comparison with naive algorithms.
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