Integration of deep feature extraction and ensemble learning for outlier detection

作者：

Highlights：

• A novel outlier detection framework using the projection principles of stacked autoencoders and probabilistic neural networks.

• An efficient technique to identify outliers in both single type outlier as well as multiple outlier type datasets.

• This article also highlights how the use of deep stacked autoencoders can enhance the performance of the standard outlier detection techniques.

摘要

•A novel outlier detection framework using the projection principles of stacked autoencoders and probabilistic neural networks.•An efficient technique to identify outliers in both single type outlier as well as multiple outlier type datasets.•This article also highlights how the use of deep stacked autoencoders can enhance the performance of the standard outlier detection techniques.

论文关键词：Deep learning,Autoencoders,Probabilistic neural networks,Ensemble learning,Outlier detection

论文评审过程：Received 27 June 2018, Revised 7 December 2018, Accepted 2 January 2019, Available online 3 January 2019, Version of Record 15 January 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.01.002