Research Area:  Machine Learning

Abstract:

Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging. Recent major work of outlier detection has focused on principle research of the local outlier factor, and there are few studies on incremental updating strategies, which are vital to outlier detection in data streams. In this paper, a novel incremental local outlier detection approach is introduced to dynamically evaluate the local outlier in the data stream. An extended local neighborhood consisting of k nearest neighbors, reverse nearest neighbors and shared nearest neighbors is estimated for each data. The theoretical evidence of algorithm complexity for the insertion of new data and deletion of old data in the composite neighborhood shows that the amount of affected data in the incremental calculation is finite. Finally, experiments performed on both synthetic and real datasets verify its scalability and outlier detection accuracy. All results show that the proposed approach has comparable performance with state-of-the-art k nearest neighbor-based methods.

Keywords:  
Outlier Detection
Data Stream
Incremental
Machine Learning
Deep Learning

Author(s) Name:  Haiqing Yao ,Xiuwen Fu,Yongsheng Yang and Octavian Postolache

Publisher name:  MDPI

DOI:  10.3390/app8081248

Volume Information:  Volume 8, Issue 8

Paper Link:   https://www.mdpi.com/2076-3417/8/8/1248