Attribute reduction is not only one of important parts researched in rough set theory

but also widely applied to many fields such as machine learning

data mining and so on.The attribute reduction method based on conditional information entropy can also be used effectively in the algebra view.However

these are two main disadvantages:this method is sensitive to noise and in some cases the obtained attribute subset may contain some redundant attributes.Therefore

in this paper

after introducing a concept of approximate reduction based on conditional information entropy in decision tables

we present an approximate reduction algorithm based on conditional information entropy(ARABCIE).The algorithm can effectively improve sensitivity to noise and properly select those redundant attributes by applications.Finally

we discuss the robustness of ARABCIE algorithm by experimenting on benchmark using several attribute subsets with different precision.