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Improving Indiscernibility Matrix Based Approach for Attribute Reduction

机译:基于难以减少的基于矩阵的方法

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The problem of finding all reducts of the attribute set of a data table has often been studied in rough set theory by using the notion of discernibility matrix. An alternative version based on the indiscernibility matrix has been used for this problem to a lesser extent due to its space and time complexity. This paper improves the indiscernibility matrix based approach for computing all reducts. Only indiscernibility matrix cells as well as subsets of the attribute set necessary for computing reducts are processed. The experiments reported in this paper show that the improved version uses less memory to store partial results and can find reducts in a shorter time.
机译:通过使用可辨别矩阵的概念,通常在粗糙集理论中研究了查找数据表的所有变化的问题。由于其空间和时间复杂性,基于屏障矩阵的替代版本已用于较小的范围。本文提高了基于难以置信的矩阵来计算所有减少方法。仅处理难以置信的矩阵单元以及计算减换所需的属性集的子集。本文报告的实验表明,改进的版本使用更少的内存来存储部分结果,并且可以在较短的时间内找到减少。

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