@ARTICLE { Rsds38689,
author = { Qingyuan, Xu and  Anhui, Tan and  Yaojin, Lin}, 
title = {A rough set method for the unicost set covering problem}, 
journal = {International Journal of Machine Learning and Cybernetics}, 
number = 	{(no. 3)}, 
pages = {781}, 
publisher = {Springer}, 
month = {06}, 
year = 2017, 
abstract = {AbstractIn this paper, we aim to provide a rough set method to deal with a class of set covering problem called the unicost set covering problem, which is a well-known problem in binary optimization. Firstly, by constructing a Multi-Relation Granular Computing (GrC) model of a given unicost set covering problem, the problem can be equivalently converted to the knowledge reduction problem in rough set theory. Thus, various kinds of efficient knowledge reduction methods in rough set theory can be used to solve the unicost set covering problem. Secondly, a commonly used reduction algorithm based on information entropy is proposed to compute a local minimum of the unicost set covering problem. Finally, the feasibility and efficiency of the proposed algorithm is examined by an example.}, 
keywords = {rough set, attribute reduction, attribute reduction, attribute order, rough set, addition strategy, discernibility matrix, discernibility matrix, knowledge reduction, reduct, rough set theory, rough sets, rough sets, addition-deletion strategy, attribute selection heuristics, core, deletion strategy, discernibility function, discernibility matrix, divide and conquer, }, 
}