Interpretable Sequence Classification via Discrete Optimization

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