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PyISC: A Bayesian anomaly detection framework for python
RISE - Research Institutes of Sweden, ICT, SICS.
RISE - Research Institutes of Sweden, ICT, SICS.
RISE - Research Institutes of Sweden, ICT, SICS.
2017 (English)In: FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference, 2017, p. 514-519Conference paper, Published paper (Refereed)
Abstract [en]

The pylSC is a Python API and extension to the C++ based Incremental Stream Clustering (ISC) anomaly detection and classification framework. The framework is based on parametric Bayesian statistical inference using the Bayesian Principal Anomaly (BPA), which enables to combine the output from several probability distributions. pylSC is designed to be easy to use and integrated with other Python libraries, specifically those used for data science. In this paper, we show how to use the framework and we also compare its performance to other well-known methods on 22 real-world datasets. The simulation results show that the performance of pylSC is comparable to the other methods. pylSC is part of the Stream toolbox developed within the STREAM project.

Place, publisher, year, edition, pages
2017. p. 514-519
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33203Scopus ID: 2-s2.0-85029514901ISBN: 9781577357872 OAI: oai:DiVA.org:ri-33203DiVA, id: diva2:1179230
Conference
30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017, 22 May 2017 through 24 May 2017
Available from: 2018-01-31 Created: 2018-01-31 Last updated: 2018-01-31Bibliographically approved

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