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pyISC: A Bayesian Anomaly Detection Framework for Python

Emruli, Blerim LU ; Olsson, Tomas and Holst, Anders (2017) 30th International Florida Artificial Intelligence Research Society Conference p.514-519
Abstract
The pyISC 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. pyISC 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 pyISC is comparable to the other methods. pyISC is part of the Stream
toolbox developed within the... (More)
The pyISC 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. pyISC 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 pyISC is comparable to the other methods. pyISC is part of the Stream
toolbox developed within the STREAM project (Less)
Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017)
pages
514 - 519
publisher
the Association for the Advancement of Artificial Intelligence (AAAI)
conference name
30th International Florida Artificial Intelligence Research Society Conference
conference location
United States
conference dates
2017-05-20 - 2017-05-24
external identifiers
  • scopus:85029514901
language
English
LU publication?
no
id
444d94e7-e49d-4e08-baf6-477bf4cbff2a
alternative location
https://cdn.aaai.org/ocs/15527/15527-68720-1-PB.pdf
date added to LUP
2025-03-31 21:54:40
date last changed
2025-04-04 14:23:32
@inproceedings{444d94e7-e49d-4e08-baf6-477bf4cbff2a,
  abstract     = {{The pyISC 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. pyISC 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<br/>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 pyISC is comparable to the other methods. pyISC is part of the Stream<br/>toolbox developed within the STREAM project}},
  author       = {{Emruli, Blerim and Olsson, Tomas and Holst, Anders}},
  booktitle    = {{Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017)}},
  language     = {{eng}},
  pages        = {{514--519}},
  publisher    = {{the Association for the Advancement of Artificial Intelligence (AAAI)}},
  title        = {{pyISC: A Bayesian Anomaly Detection Framework for Python}},
  url          = {{https://cdn.aaai.org/ocs/15527/15527-68720-1-PB.pdf}},
  year         = {{2017}},
}