Knowledge reduction with its algorithm design based on improved rough entropy

Knowledge reduction is an important problem in rough set theory. In this paper, an improved measurement is given to measure the roughness of rough set. Based on improved rough entropy, reduction theory and algorithm design are studied. Additionally, appling the weight´s idea in fuzzy theory, the conditional attribute weight in the decision table is investigated. Combing the conditional attribute weight with rough entropy, simple knowledge reduction algorithm and examples are given. Theoretical analysis and examples indicate that the complexity of this reduction algorithm is less than that based on the current positive region and the conditional information entropy.