计算机科学 ›› 2013, Vol. 40 ›› Issue (Z6): 133-135.

• 数据存储与挖掘 • 上一篇    下一篇

基于粒计算的离散化算法及其应用

史志才,夏永祥,周金祖   

  1. 上海工程技术大学电子电气工程学院 上海201620;上海工程技术大学电子电气工程学院 上海201620;上海工程技术大学电子电气工程学院 上海201620
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家自然科学基金项目(61272097),上海工程技术大学学科专业建设项目(XKCZ1212)和科技发展基金项目(2011XY16)资助

Discretization Algorithm Based on Granular Computing and its Application

SHI Zhi-cai,XIA Yong-xiang and ZHOU Jin-zu   

  • Online:2018-11-16 Published:2018-11-16

摘要: 连续数值属性的离散化是粒计算理论应用的重要步骤。首先对目前的离散化算法进行了分类讨论,提出了区间粒的概念,融合熵理论定义了区间粒的粒度,进而提出了基于粒计算的连续数值属性的离散化算法,并将该算法应用于入侵检测过程;实验结果表明该算法简洁高效,能够确保入侵检测系统的检测效果。

关键词: 粒度计算,区间粒,离散化,熵

Abstract: The discretization of continuous numerical attributes is an important step for the application of granular computing.Some current discretization algorithms are classified and discussed.The concept of section granular is proposed.Entropy theory is introduced to define the granularity of section granular.The discretization algorithm based on granular computing is proposed.This algorithm is applied to intrusion detection.The experimental results show that this algorithm is simple and effective,It can assure the accuracy of intrusion detection.

Key words: Granular computing,Section granular,Discretization,Entropy

[1] 王国胤,张清华,胡军.粒计算研究综述[J].职能系统学报,2007,2(6):8-26
[2] 张钹,张铃.粒计算未来发展方向探讨[J].重庆邮电大学学报,2010,21(5):538-540
[3] Fayyad U,Irani K.Multi-interval discretization of continuous-valued attributes for classification learning[C]∥Proceedings of the 13th International Joint Conference on Artificial Intelligence.San Mateo:Morgan Kaufmann Publisher,1993:1022-1027
[4] Kerber R C.Discretization of Numeric Attributes[C]∥Procee-dings of the 10th National Conference on Artificial Intelligence.MIT Press,1992:123-128
[5] Xia Yong-xiang,Shi Zhi-cai.An incremental SVM for intrusion detection based on key feature selection[C]∥Proceedings of the Third International Symposium on Intelligent Information Technology Application.IEEE Press,2009:205-208

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!