基于多示例学习的时序离群点检测算法研究

钱景辉; 卢屹韦

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基于多示例学习的时序离群点检测算法研究

更新时间：2026-09-04

- 基于多示例学习的时序离群点检测算法研究
- Multi-instance-learning Based Time Series Outlier Detection Algorithm
- 微电子学与计算机 2018年35卷第1期页码：46-49
- 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：
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- 纸质出版：2018
- 稿件说明：
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钱景辉, 卢屹韦. 基于多示例学习的时序离群点检测算法研究[J]. 微电子学与计算机, 2018, 35(1): 46-49, 55.

QIAN Jing-hui, LU Yi-wei. Multi-instance-learning Based Time Series Outlier Detection Algorithm[J]. 2018, 35(1): 46-49.
钱景辉, 卢屹韦. 基于多示例学习的时序离群点检测算法研究[J]. 微电子学与计算机, 2018, 35(1): 46-49, 55. DOI：

QIAN Jing-hui, LU Yi-wei. Multi-instance-learning Based Time Series Outlier Detection Algorithm[J]. 2018, 35(1): 46-49. DOI：

摘要

在FIndCBLOf算法的基础上

提出了一种基于多示例学习的FindCBLOF离群点检测算法(MIL-FindCBLof)用于时序离群点检测.算法首先对数据进行分段聚集

再利用多示例框架

封装每个对象

以此保留每个对象的属性

然后采用全局策略计算对象的因子数值

最后通过计算平均因子来确定离群序列.在实际企业的实时采集监控系统中

将MIL-FindCBLof算法与其它经典离群点检测算法进行实验对比

结果表明本文提出的MIL-FindCBLof算法相对其它算法提高了检测的全面性和准确性.

Abstract

On the basis of the FIndCBLOf algorithm

this paper proposes a FindCBLOF outlier detection algorithm (MIL-FindCBLof) for Multi instance learning based the detection of time series outliers. Firstly

the data were gathered using piecewise multi instance framework to package each object in order to preserve the properties for each object

to calculate the object factor with global strategy

to determine the outlier sequence by calculating the average factor. In the real-time monitoring system of practical enterprise MIL-FindCBLof algorithm will be compared with other classical outlier detection algorithm by experiments.The results show that the proposed MIL-FindCBLof algorithm compared with other algorithms in this paper can improve the accuracy and comprehensiveness of detection.

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Keywords

references

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Zhang W J, Zhou Z H. Multi-instance learning with distribution change[C]//28th AAAl Conference on Artificial Intelligence (AAAI'14). San Francisco, CA, USA: AAAI, 2014: 2184-2190.

Wang Z G, Zhao Z S, Weng S F, et al. Incremental multiple instance outlier detection[J]. Neural Computing % Applications, 2015, 26(4): 957-968. DOI: 957

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彭勇, 江常青, 谢丰, 等. 工业控制系统信息安全研究进展[J]. 清华大学学报: 自然科学版, 2012, 97(10): 1396-1408. DOI: 1396

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