基于双向邻居修正的局部异常因子算法

杨晓晖; 刘晓明

doi:10.11959/j.issn.1000-436x.2020119

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基于双向邻居修正的局部异常因子算法

学术论文 | 更新时间：2024-06-05

- 基于双向邻居修正的局部异常因子算法
- Local outlier factor algorithm based on correction of bidirectional neighbor
- 通信学报 2020年41卷第8期页码：130-140
- 作者机构：
  
  河北大学网络空间安全与计算机学院，河北保定 071002
- 作者简介：
  
  [ "杨晓晖（1975- ），男，河北巨鹿人，博士，河北大学教授、硕士生导师，主要研究方向为分布计算、信息安全与可信计算" ]
  [ "刘晓明（1993- ），男，河北望都人，河北大学硕士生，主要研究方向为分布式计算与信息安全" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFB0802300)
- DOI：10.11959/j.issn.1000-436x.2020119
  中图分类号： TP181
- 网络出版日期：2020-08，
  
  纸质出版日期：2020-08-25
- 稿件说明：
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杨晓晖, 刘晓明. 基于双向邻居修正的局部异常因子算法[J]. 通信学报, 2020,41(8):130-140.

Xiaohui YANG, Xiaoming LIU. Local outlier factor algorithm based on correction of bidirectional neighbor[J]. Journal on communications, 2020, 41(8): 130-140.
杨晓晖, 刘晓明. 基于双向邻居修正的局部异常因子算法[J]. 通信学报, 2020,41(8):130-140. DOI： 10.11959/j.issn.1000-436x.2020119.

Xiaohui YANG, Xiaoming LIU. Local outlier factor algorithm based on correction of bidirectional neighbor[J]. Journal on communications, 2020, 41(8): 130-140. DOI： 10.11959/j.issn.1000-436x.2020119.

摘要

针对现有离群点检测算法存在参数选取困难、效率差和精度低等问题，提出了基于双向邻居修正的局部异常因子算法。为了解决所提问题，首先提出了基于双向邻居的搜索算法，降低邻居搜索占用时间，然后使用双向邻居的修剪算法减少参数输入以及不必要的异常值计算。同时提出了基于双向邻居的修正因子，并利用反向邻居进一步提高计算精度。实验结果表明，所提算法减少了参数选取，提高了时间效率，同时基于双向邻居的修正因子使算法在合成数据集和UCI数据集上的准确率更高。

Abstract

A local outlier factor algorithm based on bidirectional neighbor correction was proposed to solve the problems of existing outlier detection algorithms such as difficulty in parameter selection

poor efficiency and low accuracy.The bidirectional neighbor searching algorithm was used to reduce the neighbor search time.Then the bidirectional neighbor pruning algorithm was used to reduce the number of parameters and unnecessary calculations.And the correction factor based on bidirectional neighbors was used to improve the calculation accuracy.Experimental results show that the proposed algorithm has better performance in parameter selection and time efficiency than other outlier detection methods.The correction factor improves the accuracy of the algorithm

in the synthetic data set and UCI data set.

关键词

Keywords

references

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