The Relationships Between Variable Precision Value and Knowledge Reduction Based on Variable Precision Rough Sets Model

摘要

The variable precision rough sets (VPRS) model is parametric and there are many types of knowledge reduction. Among the present various algorithms, β is introduced as prior knowledge. In some applications, it is not clear how to set the parameter. For that reason, it is necessary to seek an approach to realize the estimation of β from the decision table, avoiding the influence of β apriority upon the result. By studying relative discernibility in measurement of decision table, it puts forward algorithm of the threshold value of decision table's relative discernibility: choosing β within the interval of threshold value as a substitute for prior knowledge can get knowledge reduction sets under certain level of error classification, thus finally realizing self-determining knowledge reduction from decision table based on VPRS.