Approximate Reduction Based on Conditional Information Entropy in Decision Tables
Ming Yang · Dianzi xuebao · 2007
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 en- tropy 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 experiment- ing on benchmark using several attribute subsets with different precision.