LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
Artificial Intelligence
2020-11-03 v1
Abstract
In this paper, we propose and prove the theorem regarding the stability of attributes in a decision system. Based on the theorem, we propose the LRA framework for accelerating rough set algorithms. It is a general-purpose framework which can be applied to almost all rough set methods significantly . Theoretical analysis guarantees high efficiency. Note that the enhancement of efficiency will not lead to any decrease of the classification accuracy. Besides, we provide a simpler prove for the positive approximation acceleration framework.
Cite
@article{arxiv.2011.00215,
title = {LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection},
author = {Shuyin Xia and Wenhua Li and Guoyin Wang and Xinbo Gao and Changqing Zhang and Elisabeth Giem},
journal= {arXiv preprint arXiv:2011.00215},
year = {2020}
}