  We report a framework for spectroscopic follow-up design for optimizing
supernova photometric classification. The strategy accounts for the unavoidable
mismatch between spectroscopic and photometric samples, and can be used even in
the beginning of a new survey -- without any initial training set. The
framework falls under the umbrella of active learning (AL), a class of
algorithms that aims to minimize labelling costs by identifying a few,
carefully chosen, objects which have high potential in improving the classifier
predictions. As a proof of concept, we use the simulated data released after
the Supernova Photometric Classification Challenge (SNPCC) and a random forest
classifier. Our results show that, using only 12\% the number of training
objects in the SNPCC spectroscopic sample, this approach is able to double
purity results. Moreover, in order to take into account multiple spectroscopic
observations in the same night, we propose a semi-supervised batch-mode AL
algorithm which selects a set of $N=5$ most informative objects at each night.
In comparison with the initial state using the traditional approach, our method
achieves 2.3 times higher purity and comparable figure of merit results after
only 180 days of observation, or 800 queries (73% of the SNPCC spectroscopic
sample size). Such results were obtained using the same amount of spectroscopic
time necessary to observe the original SNPCC spectroscopic sample, showing that
this type of strategy is feasible with current available spectroscopic
resources. The code used in this work is available in the COINtoolbox:
https://github.com/COINtoolbox/ActSNClass .
