作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程 ›› 2011, Vol. 37 ›› Issue (14): 18-20,26. doi: 10.3969/j.issn.1000-3428.2011.14.005

• 专栏 • 上一篇    下一篇

核化空间深度包围核的模糊决策异常检测算法

张思懿 a,王士同 b   

  1. (江南大学 a. 物联网工程学院;b. 数字媒体学院,江苏 无锡 214122)
  • 收稿日期:2011-02-24 出版日期:2011-07-20 发布日期:2011-07-20
  • 作者简介:张思懿(1987-),女,硕士研究生,主研方向:模糊决策异常检测算法,人工智能,模式识别;王士同,教授、博士生导师
  • 基金资助:

    国家自然科学基金资助项目(60773206)

Fuzzy Decision Outlier Detection Algorithm of Kernelized Spatial Depth Sphere

ZHANG Si-yi a, WANG Shi-tong b   

  1. (a. School of Internet of Things Engineering; b. School of Digital Media, Jiangnan University, Wuxi 214122, China)
  • Received:2011-02-24 Online:2011-07-20 Published:2011-07-20

摘要:

根据核化空间深度异常检测算法中适用性的局限性和最小包围核算法中存在参数影响检测效率的缺点,在引入模糊决策思想下,提出一种将上述2种算法相结合的模糊决策异常检测算法。融合后的算法将2种算法的优势相结合,并用模糊决策方法提高算法的稳定性和适用性。通过在人工数据集和UCI数据集上的实验结果表明,该算法具有较好的异常检测效果。

关键词: 核化空间深度, 最小包围核, 核函数, 异常检测, 模糊决策

Abstract:

This paper proposes a new idea using the idea of the fuzzy decision. And it is based on the kernelized spatial depth and the idea of the smallest sphere, intending for the problems that kernelized spatial depth function can not have good performance on some datasets and the parameters have the influence on the effectiveness. In this way, the algorithm improves the effectiveness and robustness in outlier detection by using the advantages of the algorithm and weakening the disadvantages. This paper does some experiments on the two artificial datasets and three different UCI datasets. Results show the effectiveness of the proposed idea.

Key words: kernelized spatial depth, smallest sphere, kernelized function, outlier detection, fuzzy decision

中图分类号: