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Improved Variable Precision Rough Set Model and Its Application to Distance Learning

机译:改进的变精度粗糙集模型及其在远程学习中的应用

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Improved Variable Precision Rough Set (VPRS) is proposed to extract the significant decision rules from a Student Information Table (SIT) in the distance learning environment.Moreover,two approaches are proposed.The first approach,VPRS based on Bayesian Confirmation Measures (BCM) is presented in order to handle totally ambiguous and enhance the precision of Rough set,and to deal with multi decision classes.The second approach,the VPRS parameters are refined,especially with multi decision classes.These concepts have been demonstrated by an example.The simulated result gives good accuracy and precise information with few computational steps.
机译:提出了一种改进的可变精度粗糙集(VPRS),用于从远程学习环境中的学生信息表(SIT)中提取重要的决策规则。此外,还提出了两种方法。第一种方法是基于贝叶斯确认措施(BCM)的VPRS。提出该算法是为了处理完全模棱两可的数据并提高粗糙​​集的精度,并处理多决策类。第二种方法是对VPRS参数进行细化,尤其是使用多决策类。这些概念已通过示例进行了演示。仿真结果以较少的计算步骤即可提供良好的精度和精确的信息。

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