关键词: 可变精度粗糙集（VPRS）, 属性约简, [&beta, ]边界阈值, 支持向量机

Abstract: This paper deeply studies the threshold [β]selection based on the variable precision rough set for classification problems and gives a new method. In the face of complex multiple types of large data sets, manually setting the threshold [β] value limits its application. So this paper puts forward the concept of average contains degrees. Firstly, let the condition of average contains degrees as selecting threshold value of upper and lower approximation set. Secondly, it generates the optimal variable precision threshold according to different types of data sets. The domain boundary has large amount of condition attribute information in positive region. Experimental results show that the improved algorithm increases lower approximation set, decreases upper approximation set, and reduces the border area. On the premise of not adding training time, the results obviously improve the classification accuracy when compared with traditional Variable Precision Rough Set（VPRS）.

Key words: Variable Precision Rough Set（VPRS）, attribute reduction, boundary threshold[β], support vector machine