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Synthetic Data Generator for Classification Rules Learning

机译:分类规则学习的合成数据发生器

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A standard data set is useful to empirically evaluate classification rules learning algorithms. However, there is still no standard data set which is common enough for various situations. Data sets from the real world are limited to specific applications. The sizes of attributes, the rules and samples of the real data are fixed. A data generator is proposed here to produce synthetic data set which can be as big as the experiments demand. The size of attributes, rules, and samples of the synthetic data sets can be easily changed to meet the demands of evaluation on different learning algorithms. In the generator, related attributes are created at first. And then, rules are created based on the attributes. Samples are produced following the rules. Three decision tree algorithms are evaluated used synthetic data sets produced by the proposed data generator.
机译:标准数据集可用于凭证评估分类规则学习算法。但是,仍然没有标准数据集,这对于各种情况足够常见。来自现实世界的数据集仅限于特定应用程序。属性的大小,真实数据的规则和样本是固定的。这里提出了一种数据生成器来产生合成数据集,其可以与实验需求一样大。可以容易地改变合成数据集的属性,规则和样本的大小,以满足不同学习算法的评估需求。在生成器中,首先创建相关属性。然后,基于该属性创建规则。按规则制作样本。评估三个决策树算法使用所提出的数据发生器产生的使用合成数据集。

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