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Statistical Test for Rough Set Approximation Based on Fisher's Exact Test

机译:基于Fisher精确检验的粗糙集近似统计检验

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Rough set based rule induction methods have been applied to knowledge discovery in databases, whose empirical results obtained show that they are very powerful and that some important knowledge has been extracted from datasets. However, quantitative evaluation of lower and upper approximation are based not on statistical evidence but on rather naive indices, such as conditional probabilities and functions of conditional probabilities. In this paper, we introduce a new approach to induced lower and upper approximation of original and variable precision rough set model for quantitative evaluation, which can be viewed as a statistical test for rough set methods. For this extension, chi-square distribution, F-test and likelihood ratio test play an important role in statistical evaluation. Chi-square test statistic measures statistical information about an information table and F-test statistic and likelihood ratio statistic are used to measure the difference between two tables.
机译:基于粗糙集的规则归纳方法已经应用于数据库中的知识发现,其经验结果表明它们非常强大,并且已经从数据集中提取了一些重要的知识。但是,上下近似的定量评估不是基于统计证据,而是基于幼稚的指标,例如条件概率和条件概率函数。在本文中,我们引入了一种新的方法来对原始和可变精度粗糙集模型的上下近似进行定量评估,可以将其视为粗糙集方法的统计检验。对于此扩展,卡方分布,F检验和似然比检验在统计评估中起着重要作用。卡方检验统计量度用于度量有关信息表的统计信息,F检验统计量和似然比统计量用于度量两个表之间的差异。

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