In this paper, we propose a way to reduce the attributes of covering decision systems, which are databases characterized by covers.
Here, we solve this problem in inconsistent covering decision systems.
First, we define consistent and inconsistent covering decision systems and their attribute reductions.
Secondly, in inconsistent covering decision systems, the limitary conditional entropy of the covering is proposed and attribute reductions are defined.
However, the existing related family-based methods have to recompute reducts for dynamic covering decision information systems.
And finally, by the significance of the covering, some algorithms are designed to compute all the reducts of consistent and inconsistent covering decision systems.
Firstly, we introduce information entropy and conditional entropy of the covering and define attribute reduction by means of conditional entropy in consistent covering decision systems.
Finally, we employ several examples to illustrate the feasibility of the incremental approaches for knowledge reduction of dynamic covering decision information systems when increasing the cardinalities of coverings.
In this paper, we propose some new approaches for attribute reduction in covering decision systems from the viewpoint of information theory.
Finally, we use a discernibility matrix to design algorithms that compute all the reducts of consistent and inconsistent covering decision systems.
