  Given a labeled dataset that contains a rare (or minority) class of
of-interest instances, as well as a large class of instances that are not of
interest, how can we learn to recognize future of-interest instances over a
continuous stream? We introduce RaRecognize, which (i) estimates a general
decision boundary between the rare and the majority class, (ii) learns to
recognize individual rare subclasses that exist within the training data, as
well as (iii) flags instances from previously unseen rare subclasses as newly
emerging. The learner in (i) is general in the sense that by construction it is
dissimilar to the specialized learners in (ii), thus distinguishes minority
from the majority without overly tuning to what is seen in the training data.
Thanks to this generality, RaRecognize ignores all future instances that it
labels as majority and recognizes the recurrent as well as emerging rare
subclasses only. This saves effort at test time as well as ensures that the
model size grows moderately over time as it only maintains specialized minority
learners. Through extensive experiments, we show that RaRecognize outperforms
state-of-the art baselines on three real-world datasets that contain
corporate-risk and disaster documents as rare classes.
