一种基于多标记的局部离群点检测算法

钱景辉; 梁栋

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一种基于多标记的局部离群点检测算法

更新时间：2026-09-04

- 一种基于多标记的局部离群点检测算法
- Local Outlier Detection Algorithm Based on Multi-Label Learning
- 微电子学与计算机 2017年34卷第10期页码：110-114
- 作者机构：
  
  南京工业大学机构计算机科学与技术学院,江苏,南京,211816
- 作者简介：
- 基金信息：
- DOI：
  中图分类号：
- 纸质出版：2017
- 稿件说明：
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钱景辉, 梁栋. 一种基于多标记的局部离群点检测算法[J]. 微电子学与计算机, 2017, 34(10): 110-114.

QIAN Jing-hui, LIANG Dong. Local Outlier Detection Algorithm Based on Multi-Label Learning[J]. 2017, 34(10): 110-114.
钱景辉, 梁栋. 一种基于多标记的局部离群点检测算法[J]. 微电子学与计算机, 2017, 34(10): 110-114. DOI：

QIAN Jing-hui, LIANG Dong. Local Outlier Detection Algorithm Based on Multi-Label Learning[J]. 2017, 34(10): 110-114. DOI：

摘要

基于密度的局部离群点检测算法(LOF)不适用于解决高维度、多义性的数据集检测.通过对LOF算法的分析

提出了一种基于多标记学习(Multi-Label Learning

MLL)的局部离群点检测算法MLL-LOF(a local outlier factor based on multi-label learning).该算法采用MLL框架

首先将真实对象数据拆分成多示例包形式

然后运用退化策略及相应的权重调整

计算最终离群点因子

判别离群点.并运用实际企业的监控数据将MLL-LOF算法与其他经典的局部离群点检测算法进行了对比实验

结果表明提出的MLL-LOF算法检测的精准率、召回率、F1值以及时间效率均优于传统的局部离群点检测算法.

Abstract

The density-based local outlier detection algorithm (LOF) is not suitable for detecting the data set which is high dimension and polysemous. In this paper

we popose a local outlier detection algorithm based on multi-label learing(MLL-LOF).The main ideal of the MLL-LOF algorithm is as follows: Firstly

the real object data is divided into multi-instance by using an MLL framework

then the MLL-LOF calculates the final outlier factor and detects outliers by using degradation strategy and weight adjustment. We compare the MLL-LOF algorithm with other classical local outlier detection algorithms by using actual data set which is comes from enterprise monitoring. Experimental results show that the precision

recall

F1 and time efficiency of the MLL-LOF algorithm are superior to the traditional local outlier detection algorithm.

关键词

Keywords

references

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Breunig M, Kriegel H, Ng R, et al. Lof: Identifying density-based local outliers[C]// Proc. SIGMOD Conf. IEEE, 2000: 93-104.

Sun Huanliang, Bao Yubin, Yu Ge, et al. An algorithm based onpartition for outlier detection[J]. Journal of Software, 2006, 17(5): 1009-1016. DOI: 1009

Boutell M R, Luo J, Shen X, et al. Learning multi-label scene classification[J]. Pattern recognition, 2004, 37(9): 1757-1771. DOI: 1757

Zhou Z H, Zhang M L, Huang S J, et al. Multi-instance multi-label learning[J]. Artificial Intelligence, 2012, 176(1): 2291-2320. DOI: 2291

Dietterich T G, Lathrop R H, Lozano-Pérez T. Solving the multiple-instance problem with axis-parallel rectangles[J]. ArtificialIntelligence, 1997, 89(1/2): 31-71. DOI: 31

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