  We describe how the Fink broker early supernova Ia classifier optimizes its
ML classifications by employing an active learning (AL) strategy. We
demonstrate the feasibility of implementation of such strategies in the current
Zwicky Transient Facility (ZTF) public alert data stream. We compare the
performance of two AL strategies: uncertainty sampling and random sampling. Our
pipeline consists of 3 stages: feature extraction, classification and learning
strategy. Starting from an initial sample of 10 alerts (5 SN Ia and 5 non-Ia),
we let the algorithm identify which alert should be added to the training
sample. The system is allowed to evolve through 300 iterations. Our data set
consists of 23 840 alerts from the ZTF with confirmed classification via
cross-match with SIMBAD database and the Transient name server (TNS), 1 600 of
which were SNe Ia (1 021 unique objects). The data configuration, after the
learning cycle was completed, consists of 310 alerts for training and 23 530
for testing. Averaging over 100 realizations, the classifier achieved 89%
purity and 54% efficiency. From 01/November/2020 to 31/October/2021 Fink has
applied its early supernova Ia module to the ZTF stream and communicated
promising SN Ia candidates to the TNS. From the 535 spectroscopically
classified Fink candidates, 459 (86%) were proven to be SNe Ia. Our results
confirm the effectiveness of active learning strategies for guiding the
construction of optimal training samples for astronomical classifiers. It
demonstrates in real data that the performance of learning algorithms can be
highly improved without the need of extra computational resources or
overwhelmingly large training samples. This is, to our knowledge, the first
application of AL to real alerts data.
