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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SHOWING 1-10 OF 32 REFERENCES

Isolation Forest

The use of isolation enables the proposed method, iForest, to exploit sub-sampling to an extent that is not feasible in existing methods, creating an algorithm which has a linear time complexity with a low constant and a low memory requirement.

Isolation-Based Anomaly Detection

This article proposes a method called Isolation Forest (iForest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally different from all existing methods.

Incorporating Feedback into Tree-based Anomaly Detection

A novel technique for incorporating simple binary feedback into tree-based anomaly detectors is introduced and it is shown that the Isolation Forest algorithm can significantly improve its performance by incorporating feedback, when compared with the baseline algorithm that does not incorporate feedback.

Automatic Anomaly Detection in the Cloud Via Statistical Learning

Two novel statistical techniques for automatically detecting anomalies in cloud infrastructure data are developed that employ statistical learning to detect anomalies in both application, and system metrics.

Incorporating Expert Feedback into Active Anomaly Discovery

This paper describes an Active Anomaly Discovery method for incorporating expert feedback to adjust the anomaly detector so that the outliers it discovers are more in tune with the expert user's semantic understanding of the anomalies.

On the nature and types of anomalies: a review of deviations in data

  • Ralph Foorthuis
  • Computer Science
    International Journal of Data Science and Analytics
  • 2021
The typology facilitates the evaluation of the functional capabilities of anomaly detection algorithms, contributes to explainable data science, and provides insights into relevant topics such as local versus global anomalies.

Anomaly detection in streaming environmental sensor data: A data-driven modeling approach

LOF: identifying density-based local outliers

This paper contends that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier, called the local outlier factor (LOF), and gives a detailed formal analysis showing that LOF enjoys many desirable properties.

Isolation Forest for Anomaly Detection in Raw Vehicle Sensor Data

It can be shown that the Isolation Forest beats the Replicator Neural Network in detecting anomalies and the reduction of data transfer and what it means for the traffic between vehicle and backend.

Anomaly Detection through on-line Isolation Forest: An application to plasma etching

  • Gian Antonio SustoA. BeghiS. McLoone
  • Computer Science
    2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
  • 2017
This paper test the performance of a state-of-the-art anomaly detection technique, called Isolation Forest, on a real industrial dataset related to Etching, one of the most important semiconductor manufacturing process.