Neuro symbolic rules improve accuracy and transparency in event prediction
What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules
Machine Learning
Summary
Predicting what will happen next in a sequence of events is important in many areas, but it can be hard to understand why a computer made a certain prediction. The authors created a method called RUNES that uses clear, simple rules combined with a neural network to predict events. This method not only makes good predictions but also shows exactly which rules influenced the prediction and how adjustments were made. Their approach works well on both artificial and real-world data, making it easier to trust and explain predictions.
What this means in practice
- •For business analysts: Use interpretable rules to explain customer behavior predictions and support decision-making.
- •For software engineers: Build event prediction models that provide transparent reasoning alongside high prediction accuracy for system logs or user actions.
Authors
Tim Nico Bauerschmidt, Isabel Valera, Jilles Vreeken
Abstract
In real-world applications, being able to explain why a prediction is made is often as important - if not more so - than achieving high accuracy. However, existing methods for sequential event prediction typically prioritize accuracy at the expense of interpretability. Neuro-symbolic approaches offer a promising direction to improve interpretability by learning symbolic rules, but extending them to next-event prediction is challenging due to the presence of temporal dependencies. We introduce RULES, a neuro-symbolic model that learns sequential rules directly from data, and combine it with a residual network to form RUNES (Rule Network for Event Sequences). RULES accurately recovers ground truth rules from synthetic data and discovers compact, interpretable rule sets on real data. RUNES combines these rules additively with a residual network, achieving competitive accuracy while providing transparent access to active rules and how residuals adjust predictions.