  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 active learning based
approach, ALIF, to solve this problem by reducing the number of required labels
and tuning the detector towards the definition of anomaly provided by the user.
The proposed approach is particularly appealing in the presence of a Decision
Support System (DSS), a case that is increasingly popular in real-world
scenarios. While it is common that DSS embedded with anomaly detection
capabilities rely on unsupervised models, they don't have a way to improve
their performance: ALIF is able to enhance the capabilities of DSS by
exploiting the user feedback during common operations. ALIF is a lightweight
modification of the popular Isolation Forest that proved superior performances
with respect to other state-of-art algorithms in a multitude of real anomaly
detection datasets.
