Multigranulation rough set: A multiset based strategy
- DOI
- 10.2991/ijcis.2017.10.1.19How to use a DOI?
- Keywords
- Approximate distribution reduct; Approximate quality; Multiset; Multiple multigranulation rough set
- Abstract
A simple multigranulation rough set approach is to approximate the target through a family of binary relations. Optimistic and pessimistic multigranulation rough sets are two typical examples of such approach. However, these two multigranulation rough sets do not take frequencies of occurrences of containments or intersections into account. To solve such problem, by the motivation of the multiset, the model of the multiple multigranulation rough set is proposed, in which both lower and upper approximations are multisets. Such two multisets are useful when counting frequencies of occurrences such that objects belong to lower or upper approximations with a family of binary relations. Furthermore, not only the concept of approximate distribution reduct is introduced into multiple multigranulation rough set, but also a heuristic algorithm is presented for computing reduct. Finally, multiple multigranulation rough set approach is tested on eight UCI (University of California–Irvine) data sets. Experimental results show: 1. the approximate quality based on multiple multigranulation rough set is between approximate qualities based on optimistic and pessimistic multigranulation rough sets; 2. by comparing with optimistic and pessimistic multigranulation rough sets, multiple multigranulation rough set needs more attributes to form a reduct.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
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TY - JOUR AU - Xibei Yang AU - Suping Xu AU - Huili Dou AU - Xiaoning Song AU - Hualong Yu AU - Jingyu Yang PY - 2017 DA - 2017/01/01 TI - Multigranulation rough set: A multiset based strategy JO - International Journal of Computational Intelligence Systems SP - 277 EP - 292 VL - 10 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2017.10.1.19 DO - 10.2991/ijcis.2017.10.1.19 ID - Yang2017 ER -