Abstract
Recent papers have introduced novel approaches to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides incorrect outputs. Moreover, they have shown how to exploit the explanations obtained using state-of-the-art post-hoc explainers to identify, in a semi-automated way, the most common features that induce a predictor to make mistakes and, in turn, to mitigate their impact and improve the accuracy of the predictive model. This work starts from the assumption that frequent control-flow patterns in event logs may represent important features that characterize, and therefore explain, a certain prediction. Therefore, in this paper, we (i) employ a novel encoding able to leverage Declare constraints in PPM and compare the effectiveness of this encoding with PPM state-of-the-art encodings, in particular for the task of outcome-oriented predictions; (ii) introduce a completely automated pipeline for the identification of the most common features inducing a predictor to make mistakes; and (iii) show the effectiveness of the proposed pipeline in increasing the accuracy of the predictive model by validating it on different real-life datasets.