TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios

11/30/2021
by   Tommaso Barbariol, et al.
0

Unsupervised anomaly detection tackles the problem of finding anomalies inside datasets without the labels availability; since data tagging is typically hard or expensive to obtain, such approaches have seen huge applicability in recent years. In this context, Isolation Forest is a popular algorithm able to define an anomaly score by means of an ensemble of peculiar trees called isolation trees. These are built using a random partitioning procedure that is extremely fast and cheap to train. However, we find that the standard algorithm might be improved in terms of memory requirements, latency and performances; this is of particular importance in low resources scenarios and in TinyML implementations on ultra-constrained microprocessors. Moreover, Anomaly Detection approaches currently do not take advantage of weak supervisions: being typically consumed in Decision Support Systems, feedback from the users, even if rare, can be a valuable source of information that is currently unexplored. Beside showing iForest training limitations, we propose here TiWS-iForest, an approach that, by leveraging weak supervision is able to reduce Isolation Forest complexity and to enhance detection performances. We showed the effectiveness of TiWS-iForest on real word datasets and we share the code in a public repository to enhance reproducibility.

READ FULL TEXT
research
07/21/2020

Interpretable Anomaly Detection with DIFFI: Depth-based Feature Importance for the Isolation Forest

Anomaly Detection is one of the most important tasks in unsupervised lea...
research
07/08/2022

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

The detection of anomalous behaviours is an emerging need in many applic...
research
11/06/2018

Extended Isolation Forest

We present an extension to the model-free anomaly detection algorithm, I...
research
10/05/2022

Improved Anomaly Detection by Using the Attention-Based Isolation Forest

A new modification of Isolation Forest called Attention-Based Isolation ...
research
06/29/2020

Random Partitioning Forest for Point-Wise and Collective Anomaly Detection – Application to Intrusion Detection

In this paper, we propose DiFF-RF, an ensemble approach composed of rand...
research
02/04/2023

Unsupervised Ensemble Methods for Anomaly Detection in PLC-based Process Control

Programmable logic controller (PLC) based industrial control systems (IC...
research
09/20/2023

Distribution and volume based scoring for Isolation Forests

We make two contributions to the Isolation Forest method for anomaly and...

Please sign up or login with your details

Forgot password? Click here to reset