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Generation of Reducts Based on Nearest Neighbor Relations and Boolean Reasoning

机译:基于最近邻关系和布尔推理的减排生成

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Dimension reduction of data is an important issue in the data processing and it is needed for the analysis of higher dimensional data in the application domains. Rough set is fundamental and useful to reduce higher dimensional data to lower one. Reduct in the rough set is a minimal subset of features, which has the same discernible power as the entire features in the higher dimensional scheme. It is shown that nearest neighbor relation with minimal distance proposed here has a fundamental information for classification. In this paper, the nearest neighbor relation plays a fundamental role for generation of reducts using the Boolean reasoning. Then, two reduct generation methods based on the nearest neighbor relation with minimal distance are proposed here, which are derived from Boolean expression of nearest neighbor relations and their operations.
机译:数据的尺寸减少是数据处理中的重要问题,并且需要在应用域中分析更高的维度数据。粗糙集是基本的,可用于将更高的维度数据降低到较低的基本并且有用。在粗糙集中还原为最小的特征子集,其具有与较高尺寸方案中的整个功能相同的可辨别功率。结果表明,这里提出的最近邻居的关系具有用于分类的基本信息。在本文中,最近的邻居关系对使用布尔推理的减排来发挥基本作用。然后,此处提出了一种基于最近距离关系的两种减小生成方法,其源自最近邻居关系的布尔表达及其操作。

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