基于容错改进的邻域粗糙集属性约简算法 - 计算机应用研究 编辑部 - 《计算机应用研究》唯一官方网站

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基于容错改进的邻域粗糙集属性约简算法

Attribute reduction algorithm based on fault-tolerance improvement of neighborhood rough set

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作者 彭潇然,刘遵仁,纪俊
机构 青岛大学 a.数据科学与软件工程学院;b.计算机科学技术学院,山东 青岛 266071
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2018)08-2256-04
DOI 10.3969/j.issn.1001-3695.2018.08.004
摘要 作为Pawlak粗糙集的扩展,邻域粗糙集能有效地处理数值型的数据。但是,因为沿用了Pawlak粗糙集在构造上下近似集时的包含关系,邻域粗糙集对噪声数据的容错性很差。针对这个问题,通过引入贝叶斯最小风险决策规则,提出了一种基于容错改进的邻域粗糙集属性算法。通过与现有的算法进行比较,实验结果表明,在数据预处理阶段用该算法能得到更好的属性约简。
关键词 粗糙集;邻域粗糙集;决策粗糙集;属性约简;容错性
基金项目 国家自然科学基金资助项目(61503208)
本文URL http://www.arocmag.com/article/01-2018-08-004.html
英文标题 Attribute reduction algorithm based on fault-tolerance improvement of neighborhood rough set
作者英文名 Peng Xiaoran, Liu Zunren, Ji Jun
机构英文名 a.CollegeofDataScience&SoftwareEngineering,b.CollegeofComputerScience&Technology,QingdaoUniversity,QingdaoShandong266071,China
英文摘要 As the extension of Pawlak rough set, neighborhood rough set can effectively deal with numerical data. However, its fault tolerance is very poor to noise data, because it follows the inclusion relation which is used for constructing the upper and lower approximations in Pawlak rough set. In order to solve this problem, this paper presented a new algorithm based on fault-tolerance improvement of neighborhood rough set by introducing the Bayes decision with minimum risk. Compared with the existing algorithm, the experimental results show that the attribute reduction obtained by this proposed algorithm is better in the data pre-processing.
英文关键词 rough set; neighborhood rough set; decision rough set; attribute reduction; fault tolerance
参考文献 查看稿件参考文献
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收稿日期 2017/4/12
修回日期 2017/5/12
页码 2256-2259,2314
中图分类号 TP18
文献标志码 A