Blerim Emruli
Senior lecturer
pyISC: A Bayesian Anomaly Detection Framework for Python
Author
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
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
Publishing year
2017
Language
English
Pages
514-519
Publication/Series
Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017)
Links
Document type
Conference paper
Publisher
the Association for the Advancement of Artificial Intelligence (AAAI)
Topic
- 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
Status
Published