论文标题
通过离散优化解释的序列分类
Interpretable Sequence Classification via Discrete Optimization
论文作者
论文摘要
序列分类是预测一系列观测值的类标签的任务。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，我们学习序列分类器，这些分类器从不断发展的观察痕迹中有利于早期分类。尽管许多最新的序列分类器是神经网络，尤其是LSTMS，但我们的分类器采用有限状态自动机的形式，并通过离散优化学习。我们的基于自动机的分类器是可解释的---支持解释，反事实推理和人类在循环修改中 - 并具有强大的经验绩效。一系列目标识别和行为分类数据集的实验表明，我们学到的基于自动机学的分类器具有与基于LSTM的分类器相当的测试性能，并具有可解释的额外优势。
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.
