A Novel Approach to Establishing the VPRS Model with Threshold Parameter Selection Mechanism Based on Fuzzy Algorithms

In the past, the choices of ß values to be applied to find the ß-reducts in VPRS for an information system are somewhat arbitrary. In this study, a systematic approach to determine the threshold value ß of VPRS applied to information systems with continuous attributes is presented. The ß value is directly connected to fuzzy membership functions by implication relations and fuzzy algorithms, in which the membership functions were obtained by the standard fuzzy C-means method. The argument is that errors of system classification would occur in the fuzzy-clustering phase prior to information classification, therefore the threshold value ß should be constrained by the probability of belongingness of an object to the fuzzy clusters, i.e., through the values of membership functions.