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

TL;DR

TiWS-iForest introduces a weakly supervised adaptation of the Isolation Forest algorithm, improving its efficiency and performance for anomaly detection in resource-constrained TinyML environments.

Contribution

The paper proposes TiWS-iForest, a novel approach that leverages weak supervision to reduce complexity and enhance anomaly detection performance in TinyML scenarios.

Findings

TiWS-iForest outperforms standard Isolation Forest in low-resource settings.

The approach reduces memory and latency requirements.

Effective on real-world datasets.

Abstract

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.