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Improvement of Decision Table Attribute Reduction Algorithm Based on Discernibility Matrix

机译:基于可辨能矩阵的决策表属性缩减算法改进

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The discernibility matrix, be put forward by Skowron in 1992, is one of effective knowledge acquisition approaches for Knowledge Representation System. This method is easy to understand and manipulate, but it may cause some error when we use it to calculate the relative core and reductions of inconsistent decisions, while redundant informations are also involved. To amend the limitation of the discernibility matrix way and to decrease computation complexity of its algorithm, the new inconsistent decision table attributes reduction algorithm based on Discernibility Matrix and Fission Tactic is proposed in this paper, which is suitable for whichever a KRS, and reducing algorithm computation complexity effectively. By comparing with the origin and other existing improving algorithm, the new attributes reduction algorithm based on Discernibility Matrix is showed more concision and more efficiency.
机译:Skowron于1992年由Skow提出的可辨别矩阵是知识表示系统的有效知识获取方法之一。此方法易于理解和操作,但是当我们使用它来计算相对核心并减少不一致决策时可能会导致一些错误,而冗余信息也会涉及。为了修改可视矩阵方式的限制和降低其算法的计算复杂性,本文提出了基于可辨能矩阵和裂变策略的新的不一致决策表属性还原算法,这适用于krs和减少算法有效计算复杂性。通过与原点和其他现有的改进算法进行比较,基于可辨别矩阵的新属性还原算法显示得更多的更正和更高的效率。

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