LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
This work addresses efficiency issues in rough set-based feature selection for data mining applications, but it appears incremental as it builds on existing acceleration frameworks.
The authors tackled the problem of slow rough set algorithms for feature selection by proposing the LRA framework, which accelerates these methods without reducing classification accuracy, as proven theoretically.
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.