Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems

@article{Marcelli2022ActiveLI,
  title={Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems},
  author={Elisa Marcelli and Tommaso Barbariol and Gian Antonio Susto},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.03934}
}
The detection of anomalous behaviours is an emerging need in many applications, particularly in contexts where security and reliability are critical aspects. While the definition of anomaly strictly depends on the domain framework, it is often impractical or too time consuming to obtain a fully labelled dataset. The use of unsupervised models to overcome the lack of labels often fails to catch domain specific anomalies as they rely on general definitions of outlier. This paper suggests a new… 

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