Outlier detection with autoencoder ensembles

Jinghui Chen, Saket Sathe, Charu Aggarwal, Deepak Turaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

150 Scopus citations

Abstract

In this paper, we introduce autoencoder ensembles for unsupervised outlier detection. One problem with neural networks is that they are sensitive to noise and often require large data sets to work robustly, while increasing data size makes them slow. As a result, there are only a few existing works in the literature on the use of neural networks in outlier detection. This paper shows that neural networks can be a very competitive technique to other existing methods. The basic idea is to randomly vary on the connectivity architecture of the autoencoder to obtain significantly better performance. Furthermore, we combine this technique with an adaptive sampling method to make our approach more efficient and effective. Experimental results comparing the proposed approach with state-of-the- art detectors are presented on several benchmark data sets showing the accuracy of our approach.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
EditorsNitesh Chawla, Wei Wang
PublisherSociety for Industrial and Applied Mathematics Publications
Pages90-98
Number of pages9
ISBN (Electronic)9781611974874
DOIs
StatePublished - 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Publication series

NameProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017

Other

Other17th SIAM International Conference on Data Mining, SDM 2017
Country/TerritoryUnited States
CityHouston
Period4/27/174/29/17

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications

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