Self-structured confabulation network for fast anomaly detection and reasoning

Qiuwen Chen, Qing Wu, Morgan Bishop, Richard Linderman, Qinru Qiu

Research output: Chapter in Book/Entry/PoemConference contribution

5 Scopus citations


Inference models such as the confabulation network are particularly useful in anomaly detection applications because they allow introspection to the decision process. However, building such network model always requires expert knowledge. In this paper, we present a self-structuring technique that learns the structure of a confabulation network from unlabeled data. Without any assumption of the distribution of data, we leverage the mutual information between features to learn a succinct network configuration, and enable fast incremental learning to refine the knowledge bases from continuous data streams. Compared to several existing anomaly detection methods, the proposed approach provides higher detection performance and excellent reasoning capability. We also exploit the massive parallelism that is inherent to the inference model and accelerate the detection process using GPUs. Experimental results show significant speedups and the potential to be applied to real-time applications with high-volume data streams.

Original languageEnglish (US)
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479919604, 9781479919604, 9781479919604, 9781479919604
StatePublished - Sep 28 2015
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: Jul 12 2015Jul 17 2015

Publication series

NameProceedings of the International Joint Conference on Neural Networks


OtherInternational Joint Conference on Neural Networks, IJCNN 2015


  • Logic gates

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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