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工程科学与技术:2024,56(1):65-81
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基于不完备混合序信息系统的增量式属性约简
(1.淮南师范学院 计算机学院,安徽 淮南 232038;2.南京航空航天大学 电子与信息工程学院,江苏 南京 211106)
Incremental Attribute Reduction Algorithm Based on Incomplete Hybrid Order Information System
(1.School of Computer Sci., Huainan Normal Univ., Huainan 232038, China;2.College of Electronic and Info. Eng., Nanjing Univ. of Aeronautics and Astronautics, Nanjing 211106, China)
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投稿时间:2022-11-07    
中文摘要: 由于大数据环境下数据呈现出动态更新的特征,因此,增量式属性约简已成为粗糙集理论的重点研究方向。不完备混合型有序信息系统是一种常见的信息系统类型,然而,目前少有增量式属性约简方面的相关研究,针对这一问题,本文在不完备混合型有序信息系统下提出一种对象更新情形的增量式属性约简算法。首先,针对不完备混合型有序信息系统提出了邻域容差优势关系,基于该二元关系建立了一种新的邻域优势粗糙集模型。其次,在其基础上定义了邻域优势条件熵,并利用邻域优势条件熵作为启发式函数设计出一种不完备混合型有序信息系统的非增量式属性约简算法。然后,利用矩阵的形式重构了邻域容差优势关系和邻域优势条件熵,针对不完备混合型有序信息系统对象的动态变化,基于矩阵的计算策略分别研究了邻域优势条件熵随信息系统对象增加和对象减少时的增量式更新。最后,利用邻域优势条件熵的更新机制分别提出了不完备混合型有序信息系统对象增加和对象减少时属性约简的增量式更新算法。实验结果表明:1)与非增量式算法相比,所提出的增量式算法约简属性数量平均降低了3.6%,分类精度平均提升了2.4%,属性约简的效率平均提升了约10倍;2)与同类型增量式算法相比,所提出的增量式算法约简属性数量平均降低了9.0%,分类精度平均提升了2.1%,属性约简的平均效率提升了94%。因此,本文所提出增量式算法无论在属性约简结果和属性约简效率上都有着更高的性能。
Abstract:Because the data in the big data environment presents the characteristics of dynamic updating, incremental attribute reduction is attracting increasing research attention in the field of rough set theory. As a common information system, the incomplete hybrid ordered information systems (IHOIS) is still without the study of the incremental attribute reduction. To address this issue, an incremental attribute reduction algorithm for object updates is proposed for IHOIS in this paper. Firstly, a neighborhood tolerance dominance relation is proposed to build a new neighborhood dominance rough set based on binary relation. In succession, a neighborhood dominance conditional entropy is defined and further serves as a heuristic function to design a non-incremental attribute reduction algorithm for IHOIS. Then, the neighborhood tolerance dominance relation and neighborhood dominance conditional entropy were reconstructed in the form of matrices. In response to the dynamic updates of the IHOIS, matrix-based calculation strategies are applied to study the incremental updates of neighborhood dominance conditional entropy with both the increasing and decreasing of the information system objects. Finally, the update mechanism of neighborhood dominance conditional entropy is utilized to develop the incremental update algorithms for attribute reduction for both the increasing and decreasing of the IHOIS objects. The experimental results show that compared with the non-incremental algorithm, the incremental algorithm reduces the number of attributes by 3.6% on average, improves the classification accuracy by 2.4% on average, and improves the efficiency of attribute reduction by about 10 times on average. Compared with other incremental algorithms, the proposed incremental algorithm reduces the number of attributes by 9.0% on average, improves the classification accuracy by 2.1% on average, and increases the average efficiency of attribute reduction by 94%. Based on the reported results, it can be concluded that the proposed incremental algorithms have higher performance in both performance and efficiency of the attribute reduction task.
文章编号:202201214     中图分类号:    文献标志码:
基金项目:安徽省高校自然科学研究重点项目(KJ2018A0469;KJ2021A0972)
作者简介:第一作者:陈宝国(1978-),男,副教授,硕士.研究方向:数据挖掘;粗糙集与粒计算.E-mail:bgchen0706@163.com;通信作者:陈磊,E-mail:13855491762@163.com
引用文本:
陈宝国,陈磊,邓明,陈金林.基于不完备混合序信息系统的增量式属性约简[J].工程科学与技术,2024,56(1):65-81.
CHEN Baoguo,CHEN Lei,DENG Ming,CHEN Jinlin.Incremental Attribute Reduction Algorithm Based on Incomplete Hybrid Order Information System[J].Advanced Engineering Sciences,2024,56(1):65-81.
相关附件:   增强出版附件1.doc