Attribute Reduction Method of Covering Rough Set Based on Dependence Degree
- DOI
- 10.2991/ijcis.d.210419.002How to use a DOI?
- Keywords
- Covering rough sets; Attribute reduction; Dependence degree; Local dependence degree; ε-Boolean identification matrix
- Abstract
Attribute reduction is a hot topic in the field of data mining. Compared with the traditional methods, the attribute reduction algorithm based on covering rough set is more suitable for dealing with numerical data. However, this kind of algorithm is still not efficient enough to deal with large-scale data. In this paper, we firstly propose -Boolean identification matrix of covering rough sets, give the calculation methods of dependence degree and local dependence degree, and further discuss their properties. Secondly, we give two attribute reduction algorithms based on dependence degree and local dependence degree, respectively. Finally, we test the performance of the algorithm through several UCI data sets. Experimental results show that the efficiency of our algorithm has been greatly improved. So it is more suitable for handling large-scale data processing problems, and can have wide application value.
- Copyright
- © 2021 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Li Fachao AU - Ren Yexing AU - Jin Chenxia PY - 2021 DA - 2021/04/30 TI - Attribute Reduction Method of Covering Rough Set Based on Dependence Degree JO - International Journal of Computational Intelligence Systems SP - 1419 EP - 1425 VL - 14 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.210419.002 DO - 10.2991/ijcis.d.210419.002 ID - Fachao2021 ER -