华南理工大学学报(自然科学版) ›› 2013, Vol. 41 ›› Issue (1): 83-88.doi: 10.3969/j.issn.1000-565X.2013.01.013

• 计算机科学与技术 • 上一篇    下一篇

基于属性分辨度的不完备决策表属性约简算法

纪霞1,2 李龙澍1,2 齐平1   

  1. 1. 安徽大学 计算智能与信号处理教育部重点实验室, 安徽 合肥 230039; 2. 安徽大学 计算机科学与技术学院, 安徽 合肥 230601
  • 收稿日期:2012-01-20 修回日期:2012-08-02 出版日期:2013-01-25 发布日期:2012-12-03
  • 通信作者: 纪霞(1982-),女,博士,讲师,主要从事不精确信息处理、粗糙集理论及其应用研究. E-mail:jixia1983@163.com
  • 作者简介:纪霞(1982-),女,博士,讲师,主要从事不精确信息处理、粗糙集理论及其应用研究.
  • 基金资助:

    安徽省自然科学基金资助项目(090412054);安徽省高等学校省级自然科学基金资助项目(KJ2011Z020);安徽大学博士科研启动基金资助项目(33190081)

Attribute Reduction Algorithm for Incomplete Decision Table Based onAttribute Discernibility

Ji Xia1,2 Li Long-shu1,2 Qi Ping1   

  1. 1. Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education, Anhui University, Hefei 230039,Anhui, China; 2. School of Computer Science and Technology, Anhui University, Hefei 230601, Anhui, China
  • Received:2012-01-20 Revised:2012-08-02 Online:2013-01-25 Published:2012-12-03
  • Contact: 纪霞(1982-),女,博士,讲师,主要从事不精确信息处理、粗糙集理论及其应用研究. E-mail:jixia1983@163.com
  • About author:纪霞(1982-),女,博士,讲师,主要从事不精确信息处理、粗糙集理论及其应用研究.
  • Supported by:

    安徽省自然科学基金资助项目(090412054);安徽省高等学校省级自然科学基金资助项目(KJ2011Z020);安徽大学博士科研启动基金资助项目(33190081)

摘要: 针对现有不完备决策表属性约简算法复杂度较高的问题,提出了基于属性分辨度的属性约简算法. 文中分析了不完备决策表中条件属性相对于决策重要性的外在表现,提出了属性分辨度的概念,并给出了属性分辨度随着约简属性集的变化而动态更新的计算方法. 该算法在属性约简过程中会不断删除已经属于正域的对象或不影响正域计算的相容块,通过降低样本数据的规模来减少计算耗时,加速属性约简. 理论分析和仿真实验表明,文中算法是有效的,并且算法复杂度优于现有的不完备决策表属性约简算法.

关键词: 不完备决策表, 属性约简, 相容块, 属性分辨度

Abstract:

Proposed in this paper is a novel attribute reduction algorithm based on the attribute discernibility to re-duce the high computational complexity of the existing attribute reduction algorithms for incomplete decision tables.In the investigation, the external manifestation of condition attribute significance relative to the decision attribute inincomplete decision tables is analyzed, the attribute discernibility is defined, and the calculating method of dynamicupdate of attribute discernibility with the change of reduction subset is proposed. During the attribute reduction, thealgorithm continuously deletes the objects belonging to the positive domain or the consistent blocks having no effecton the positive domain calculation. Thus, the data scale and the time consumption decreases, and the attribute re-duction quickens. Theoretical analyses and simulation experiments demonstrate that the proposed attribute reductionalgorithm for incomplete decision tables is effective and is superior to the existing algorithms in terms of time com-plexity.

Key words: incomplete decision table, attribute reduction, consistent block, attribute discernibility