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Supporting E-Learning System with Modified Bayesian Rough Set Model

机译:改进的贝叶斯粗糙集模型支持的电子学习系统

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The increasing development of Internet, especially Web-based learning is one of the most important issues. In this paper, a new application on Bayesian Rough Set (BRS) model for give information about learner performance is formulated. To enhance the precision of original rough set and to deal with both two decision classes and multi decision classes, we modify BRS model based on Bayesian Confirmation Measures (BCM). The experimental results are compared with that got by other methods. The quality of the proposed BRS model can be evaluated using discriminant index of decision making, which is suitable for providing appropriate decision rules to the learners with high discriminant index.
机译:Internet的不断发展,尤其是基于Web的学习是最重要的问题之一。本文提出了一种在贝叶斯粗糙集(BRS)模型上的新应用,该模型可提供有关学习者表现的信息。为了提高原始粗糙集的精度并同时处理两个决策类和多个决策类,我们基于贝叶斯确认度量(BCM)修改了BRS模型。将实验结果与其他方法进行了比较。所提出的BRS模型的质量可以使用决策的判别指标进行评估,这适合于为具有高判别指标的学习者提供适当的决策规则。

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