Multi-instance-learning Based Time Series Outlier Detection Algorithm

QIAN Jing-hui; LU Yi-wei

Chinese

您当前的位置：

首页 >

文章列表页 >

Multi-instance-learning Based Time Series Outlier Detection Algorithm

更新时间：2026-09-04

- Multi-instance-learning Based Time Series Outlier Detection Algorithm
- Vol. 35, Issue 1, Pages: 46-49(2018)
- 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：
  CLC：
- Published：2018
- 稿件说明：
移动端阅览
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.

关键词

Keywords

references

Ruffo G. Learning single and multiple instance decision trees for computer security applications[D]. Torino: University of Turin, 2000.

Wang J, Zucker J D. Solving the multiple-instance problem: a lazy learning approach[C]// 17th International Conference on Machine Learning. San Francisco, CA, USA: Morgan Kaufmann, 2000: 1119-1125.

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

Zhou Z H, Zhang M L. Neural networks for multi-instance learning[R]. Nanjing: Nanjing University, 2002.

He Z Y, Xu X F, Deng S C. Discovering cluster-based local outliers[J]. Pattern Recognition Lett, 2003, 24(9/10): 1641-1650. DOI: 1641

Wei X S, Wu J, Zhou Z H. Scalable multi-instance learning[C]//14th IEEE International Conference on Data Mining (ICDM'14). Piscataway, NJ, USA: IEEE, 2014: 1037-1042.

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

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

Aerial small object detection algorithm based on improved RT-DETR

Research on the design of blockchain-based AI-generated content traceability framework

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰