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Blerim Emruli. Foto

Blerim Emruli

Universitetslektor

Blerim Emruli. Foto

pyISC: A Bayesian Anomaly Detection Framework for Python

Författare

  • Blerim Emruli
  • Tomas Olsson
  • Anders Holst

Summary, in English

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

Publiceringsår

2017

Språk

Engelska

Sidor

514-519

Publikation/Tidskrift/Serie

Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017)

Dokumenttyp

Konferenspaper i proceeding

Förlag

the Association for the Advancement of Artificial Intelligence (AAAI)

Ämne

  • Probability Theory and Statistics

Conference name

30th International Florida Artificial Intelligence Research Society Conference

Conference date

2017-05-20 - 2017-05-24

Conference place

, United States

Aktiv

Published