[1]陈曼如,张楠,童向荣,等.集值信息系统的快速正域约简[J].智能系统学报,2019,14(3):471-478.[doi:10.11992/tis.201804059]
CHEN Manru,ZHANG Nan,TONG Xiangrong,et al.Quick positive region reduction in set-valued information systems[J].CAAI Transactions on Intelligent Systems,2019,14(3):471-478.[doi:10.11992/tis.201804059]
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CHEN Manru,ZHANG Nan,TONG Xiangrong,et al.Quick positive region reduction in set-valued information systems[J].CAAI Transactions on Intelligent Systems,2019,14(3):471-478.[doi:10.11992/tis.201804059]
集值信息系统的快速正域约简
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第3期
页码:
471-478
栏目:
学术论文—智能系统
出版日期:
2019-05-05
- Title:
- Quick positive region reduction in set-valued information systems
- Keywords:
- attribute reduction; rough set; set-valued information systems; feature selection; heuristic algorithm; positive region reduction; quick algorithm reduction; rough approximations
- 分类号:
- TP18
- 摘要:
- 针对集值信息系统正域约简算法在大规模数据集下的运行效率问题，提出一种基于启发式的集值信息系统快速正域约简算法。通过研究属性和对象在约简过程中对算法运行效率产生的影响，在集值信息系统中引入属性无关性和属性重要度保序性的相关定义，介绍了使得算法运行效率提升的相关定理、快速算法和应用实例。通过实验对提出算法的有效性进行分析和验证。实验表明，提出算法的运行效率优于原始算法的运行效率。
- Abstract:
- This study aims to propose a quick positive reduction algorithm based on the heuristic method to increase the efficiency of the set-valued positive reduction algorithm under large-scale data. The definitions of attribute independence and attribute importance isotonicity are introduced in the set-valued information system by investigating the influence of an attribute and object on the efficiency of algorithm during the reduction process, and the relevant theorem, fast algorithm, and practical example for improving the efficiency of the algorithm are introduced. Finally, the experimental results show the efficiency and effectiveness of the proposed method and its better efficiency in comparison to that of the original algorithm.
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备注/Memo
收稿日期:2018-04-27。
基金项目:国家自然科学基金项目（61403329，61572418，61702439，61572419，61502410）；山东省自然科学基金项目（ZR2018BA004，ZR2016FM42）；烟台大学研究生科技创新基金项目（YDZD1807）.
作者简介:陈曼如,女,1993年生,硕士研究生,主要研究方向为粗糙集、数据挖掘与机器学习;张楠,男,1979年生,博士研究生,主要研究方向为粗糙集、认知信息学与人工智能;童向荣,男,1975年生,教授,主要研究方向为多Agent系统、分布式人工智能与数据挖掘技术。发表学术论文50余篇,被SCI检索2篇、EI检索20余篇。
通讯作者:张楠.E-mail:zhangnan0851@163.com
更新日期/Last Update:
1900-01-01