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Recent papers have introduced a novel approach to explain why a PredictiveProcess Monitoring (PPM) model for outcome-oriented predictions provides wrongpredictions. Moreover, they have shown how to exploit the explanations,obtained using state-of-the art post-hoc explainers, to identify the mostcommon features that induce a predictor to make mistakes in a semi-automatedway, and, in turn, to reduce the impact of those features and increase theaccuracy of the predictive model. This work starts from the assumption thatfrequent control flow patterns in event logs may represent important featuresthat characterize, and therefore explain, a certain prediction. Therefore, inthis paper, we (i) employ a novel encoding able to leverage DECLARE constraintsin Predictive Process Monitoring and compare the effectiveness of this encodingwith Predictive Process Monitoring state-of-the art encodings, in particularfor the task of outcome-oriented predictions; (ii) introduce a completelyautomated pipeline for the identification of the most common features inducinga predictor to make mistakes; and (iii) show the effectiveness of the proposedpipeline in increasing the accuracy of the predictive model by validating it ondifferent real-life datasets.

Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles