TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML
scenarios
- URL: http://arxiv.org/abs/2111.15432v1
- Date: Tue, 30 Nov 2021 14:24:27 GMT
- Title: TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML
scenarios
- Authors: Tommaso Barbariol and Gian Antonio Susto
- Abstract summary: Isolation Forest is a popular algorithm able to define an anomaly score by means of an ensemble of peculiar trees called isolation trees.
We show that the standard algorithm might be improved in terms of memory requirements, latency and performances.
We propose TiWS-iForest, an approach that, by leveraging weak supervision, is able to reduce Isolation Forest complexity and to enhance detection performances.
- Score: 2.7285752469525315
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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