Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles
This work addresses accuracy improvement in PPM for outcome-oriented predictions, representing an incremental advancement with automated error analysis.
The paper tackles the problem of improving the accuracy of Predictive Process Monitoring (PPM) classifiers for outcome-oriented predictions by introducing a novel encoding based on DECLARE constraints and an automated pipeline to identify and mitigate error-inducing features, resulting in increased accuracy validated on real-life datasets.
Recent papers have introduced a novel approach to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides wrong predictions. Moreover, they have shown how to exploit the explanations, obtained using state-of-the art post-hoc explainers, to identify the most common features that induce a predictor to make mistakes in a semi-automated way, and, in turn, to reduce the impact of those features and increase 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 Predictive Process Monitoring and compare the effectiveness of this encoding with Predictive Process Monitoring 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.