  Sequence classification is the task of predicting a class label given a
sequence of observations. In many applications such as healthcare monitoring or
intrusion detection, early classification is crucial to prompt intervention. In
this work, we learn sequence classifiers that favour early classification from
an evolving observation trace. While many state-of-the-art sequence classifiers
are neural networks, and in particular LSTMs, our classifiers take the form of
finite state automata and are learned via discrete optimization. Our
automata-based classifiers are interpretable---supporting explanation,
counterfactual reasoning, and human-in-the-loop modification---and have strong
empirical performance. Experiments over a suite of goal recognition and
behaviour classification datasets show our learned automata-based classifiers
to have comparable test performance to LSTM-based classifiers, with the added
advantage of being interpretable.
