Exploring the Suitability of Rule-Based Classification to Provide Interpretability in Outcome-Based Process Predictive Monitoring

Abstract

The development of models for process outcome prediction using event logs has evolved in the literature with a clear focus on performance improvement. In this paper, we take a different perspective, focusing on obtaining interpretable predictive models for outcome prediction. We propose to use association rule-based classification, which results in inherently interpretable classification models. Although association rule mining has been used with event logs for process model approximation and anomaly detection in the past, its application to an outcome-based predictive model is novel. Moreover, we propose two ways of visualising the rules obtained to increase the interpretability of the model. First, the rules composing a model can be visualised globally. Second, given a running case on which a prediction is made, the rules influencing the prediction for that particular case can be visualised locally. The experimental results on real world event logs show that in most cases the performance of the rule-based classifier (RIPPER) is close to the one of traditional machine learning approaches. We also show the application of the global and local visualisation methods to real world event logs.