Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model

Authors

Yan Gao, Changwei Lv, Zhengjiang Wu^*,

College of Computer Science and Technology, Henan Polytechnic University, No. 2001 Century Avenue, Jiaozuo, Henan 454003, P.R. China

^*Corresponding author. Email: wuzhengjiang@hpu.edu.cn

DOI

10.2991/ijcis.d.200915.004How to use a DOI?

Keywords

Neighborhood rough set; Boolean matrix; Attribute reduction; GPU

Abstract

Neighborhood rough set is a powerful tool to deal with continuous value information systems. Graphics processing unit (GPU) computing can efficiently accelerate the calculation of the attribute reduction and approximation sets based on matrix. In this paper, we rewrite neighborhood approximation sets in the matrix-based form. Based on the matrix-based neighborhood approximation sets, we propose the relative dependency degree of attributes and the corresponding algorithm (DBM). Furthermore, we design the reduction algorithm (ARNI) for continuous value information systems. Compared with other algorithms, ARNI can effectively remove redundant attributes, and less affect the classification accuracy. On the other hand, the experiment shows ARNI based on the matrixing rough set model can significantly speed up by GPU. The speedup is many times over the central processing unit implementation.

Copyright

© 2020 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  - Yan Gao
AU  - Changwei Lv
AU  - Zhengjiang Wu
PY  - 2020
DA  - 2020/09/21
TI  - Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model
JO  - International Journal of Computational Intelligence Systems
SP  - 1473
EP  - 1482
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200915.004
DO  - 10.2991/ijcis.d.200915.004
ID  - Gao2020
ER  -