# Interpretable Sequence Classification via Discrete Optimization

arXiv: https://arxiv.org/abs/2010.02819 · HTML: https://mlsanity.com/paper/2010.02819/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Learning Reward Machines: A Study in Partially Observable Reinforcement Learning](https://mlsanity.com/paper/2112.09477/) (2021)
- [Learning Interpretable Temporal Properties from Positive Examples Only](https://mlsanity.com/paper/2209.02650/) (2022)
- [Detecting Hidden Triggers: Mapping Non-Markov Reward Functions to Markov](https://mlsanity.com/paper/2401.11325/) (2024)
- [Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior](https://mlsanity.com/paper/2301.02952/) (2023)
- [Uncertainty-Aware Signal Temporal Logic Inference](https://mlsanity.com/paper/2105.11545/) (2021)
- [FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata](https://mlsanity.com/paper/2203.16331/) (2022)
- [Learning Automata-Based Complex Event Patterns in Answer Set Programming](https://mlsanity.com/paper/2208.14820/) (2022)
- [EXCODER: EXplainable Classification Of DiscretE time series Representations](https://mlsanity.com/paper/2602.13087/) (2026)
- [PrefixGuard: From LLM-Agent Traces to Online Failure-Warning Monitors](https://mlsanity.com/paper/2605.06455/) (2026)
- [Unsupervised Automata Learning via Discrete Optimization](https://mlsanity.com/paper/2303.14111/) (2023)
- [Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference](https://mlsanity.com/paper/2306.09966/) (2023)
- [Hamming Encoder: Mining Discriminative k-mers for Discrete Sequence Classification](https://mlsanity.com/paper/2310.10321/) (2023)
- [Goal Recognition via Linear Programming](https://mlsanity.com/paper/2404.07934/) (2024)
- [Augmenting train maintenance technicians with automated incident diagnostic suggestions](https://mlsanity.com/paper/2408.10288/) (2024)
- [Learning Interpretable Classifiers for PDDL Planning](https://mlsanity.com/paper/2410.10011/) (2024)
- [Learning Predictive Checklists with Probabilistic Logic Programming](https://mlsanity.com/paper/2411.16790/) (2024)
- [Analysis of Customer Journeys Using Prototype Detection and Counterfactual Explanations for Sequential Data](https://mlsanity.com/paper/2505.11086/) (2025)
- [Online Dynamic Goal Recognition in Gym Environments](https://mlsanity.com/paper/2509.23244/) (2025)
- [Active Learning of Symbolic Automata Over Rational Numbers](https://mlsanity.com/paper/2511.12315/) (2025)

## Abstract (arXiv)

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.