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中文摘要：
变精度粗集模型是一种有参数的粗集扩充模型．目前，基于变精度粗集模型知识约简和学习的各种算法中，参数届值都是作为领域的先验知识而被直接引入．而β值不同，约简的结果一般也不同，因此有必要寻求一种方法，实现从原始数据集本身出发完成对β值的估计和选择，从而摆脱β先验知识对结果的影响．结合决策表确定性度量和决策表相对辨识性方法，分别研究了β值的估计和选择以及参数β值对约简结果的影响．通过实例进行了分析，结果表明在利用变精度粗集模型提高系统容错度时，厣值选择是必要的。
英文摘要：
The variable precision rough set model is parametric and there are many types of knowledge reduction in the area of rough sets such as distribution reduction and assignment reduction. Among the present various algorithms of knowledge reduction based on variable precision rough set model, β is always introduced directly as prior domain 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 and choosing of β from the original data set, avoiding the influence of β apriority upon the resuit. Based on certainty measure and the relative discernibility in measurement of decision table, two methods are developed to estimate and to choose the β value. It is necessary to choose β value by analyzing the influences of the result of knowledge reduction based on variable precision rough set model.
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