Attribute Reduction Method of Covering Rough Set Based on Dependence Degree

Authors

Li Fachao^{1, 2}, Ren Yexing^{2, *,} , Jin Chenxia¹

¹School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, 050018, China

²College of Science, Hebei University of Science and Technology, Shijiazhuang, 050018, China

^*Corresponding author. Email: x960312@yeah.net

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 ε-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/).

Download article (PDF)
View full text (HTML)

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  -