《计算机应用研究》|Application Research of Computers

基于非平衡数据下不完备混合型信息系统的属性约简

Attribute reduction of incomplete hybrid information system based on unbalanced data

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作者 姚晟,李初宴,陈悦
机构 安徽大学 a.计算机科学与技术学院;b.计算智能与信号处理教育部重点实验室,合肥 230039
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文章编号 1001-3695(2021)05-009-1331-05
DOI 10.19734/j.issn.1001-3695.2020.07.0178
摘要 完备混合型信息系统下的粗糙集模型是传统粗糙集模型的重要扩展,目前关于非平衡数据属性约简的研究仅限于完备混合型的粗糙集模型。针对这一问题,提出一种基于不完备混合型信息系统的非平衡数据属性约简。本文首先将传统的粗糙集模型进行推广,提出不完备混合型信息系统下的粗糙集模型;然后针对数据的非平衡性,根据上下边界区域和类分布的不均匀性定义了一种新的属性重要度;在基于区别矩阵的基础上设计出一种非平衡数据的属性约简算法。实验分析表明该算法针对不完备非平衡数据的属性约简具有一定的有效性和优越性。
关键词 粗糙集; 不完备混合型信息系统; 非平衡数据; 属性约简
基金项目 国家自然科学基金资助项目(61602004,61300057)
安徽省自然科学基金资助项目(1508085MF127)
安徽省高等学校自然科学研究重点资助项目(KJ2016A041)
安徽大学信息保障技术协同创新中心公开招标课题(ADXXBZ20145,ADXXBZ20146)
安徽大学博士科研启动基金资助项目(J10113190072)
本文URL http://www.netgaindomains.com/article/01-2021-05-009.html
英文标题 Attribute reduction of incomplete hybrid information system based on unbalanced data
作者英文名 Yao Sheng, Li Chuyan, Chen Yue
机构英文名 a.School of Computer Science & Technology,b.Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education,Anhui University,Hefei 230039,China
英文摘要 The rough set model under complete hybrid information system is an important extension of the traditional rough set model. However, the research on the attribute reduction of unbalanced data is limited to the complete hybrid rough set model. To solve this problem, this paper proposed an unbalanced data attribute reduction based on incomplete hybrid information system. This paper generalized the traditional rough set model, and then proposed the rough set model under incomplete hybrid information system. Then, it defined a new attribute importance according to the heterogeneity of upper and lower boundary regions and class distribution for the unbalanced data. Finally, this paper designed an algorithm for attribute reduction of unbalanced data based on discernible matrix. The experimental results show that the algorithm is effective and advantageous for attribute reduction of incomplete and unbalanced data.
英文关键词 rough set; incomplete hybrid information system; unbalanced data; attribute reduction
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收稿日期 2020/7/2
修回日期 2020/8/12
页码 1331-1335
中图分类号 TP391
文献标志码 A
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