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.

翻译：序列分类是预测等级标签的任务, 给出一系列观测序列。 在医疗保健监测或入侵检测等许多应用中, 早期分类对于迅速干预至关重要 。 在这项工作中, 我们从不断演变的观测跟踪中学习了有利于早期分类的序列分类方法。 许多最先进的序列分类方法都是神经网络, 特别是 LSTMS, 我们的分类方法采取有限的状态自动成像形式, 并通过离散优化学习。 我们的基于自动成像的分类方法是可解释的辅助解释、 反事实推理、 流动中的人为修改方法, 并且具有很强的经验性能。 对一组基于目标的识别和行为分类数据集的实验表明,我们所学过的基于自动成像的分类方法具有与基于 LSTM 的分类方法可比的测试性能, 并具有可解释的附加优势 。