An adjustable multigranulation fuzzy rough set

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• 作者：Yan Chen
• 关键词：Fuzzy rough set ; Fuzzy decision tables ; Consistency measure ; Attribute reduction
• 刊名：International Journal of Machine Learning and Cybernetics
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：7
• 期：2
• 页码：267-274
• 全文大小：453 KB
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• 作者单位：Yan Chen (1)
  
  1. Software School, North University of China, Taiyuan, Shanxi, China
  
• 刊物类别：Engineering
• 刊物主题：Artificial Intelligence and Robotics
  Statistical Physics, Dynamical Systems and Complexity
  Computational Intelligence
  Control , Robotics, Mechatronics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1868-808X

文摘

Multigranulation rough set is a novel generalization of Pawlak’s rough set through using multiple granular structures instead of single granular structure. By considering the maximal and minimal operators used in the optimistic and pessimistic multigranulation fuzzy rough sets, we devote to present an adjustable multigranulation fuzzy rough set. Such new model is constructed on a parameterized binary operator, which is an improvement of maximal and minimal operators. It is shown that both optimistic and pessimistic multigranulation fuzzy rough sets are special cases of adjustable multigranulation fuzzy rough set. Moreover, we also derive an approximation quality significance measure and design a forward greedy algorithm for granular structures selection. Experiments show the validity of the proposed algorithm from search strategy in the meaning of parameters used in adjustable multigranulation fuzzy rough sets.