  We present the Active Galactic Nuclei (AGN) classifier as currently
implemented within the Fink broker. Features were built upon summary statistics
of available photometric points, as well as color estimation enabled by
symbolic regression. The learning stage includes an active learning loop, used
to build an optimized training sample from labels reported in astronomical
catalogs. Using this method to classify real alerts from the Zwicky Transient
Facility (ZTF), we achieved 98.0% accuracy, 93.8% precision and 88.5% recall.
We also describe the modifications necessary to enable processing data from the
upcoming Vera C. Rubin Observatory Large Survey of Space and Time (LSST), and
apply them to the training sample of the Extended LSST Astronomical Time-series
Classification Challenge (ELAsTiCC). Results show that our designed feature
space enables high performances of traditional machine learning algorithms in
this binary classification task.
