Abstract: Knowledge reduction is an important research issue in rough set theory.Rough set theory is an efficient mathematical tool for further reducing redundancy.The main limitation of traditional rough set theory is the lack of effective methods for dealing with real-valued data.However,practical data sets are always continuous.This has been addressed by employing discretization methods,which may result in information loss.This paper investigated one approach combining tolerance relation together with rough set theory.In order to enhance the ability to adapt to the noise data,this paper explored the knowledge reduction algorithm based on variable precision tolerance rough set theory.The cha-racteristics of parameter were analyzed.The relationship between the classification quality and parameter interval was described,and the parameter value was extended to interval range.The experimental results demonstrate that our proposed algorithm and the related theory are effective.

Key words: Rough set theory,Tolerance relation,Variable precision,Parameter interval,Degree of dependency of feature