The holy grail in process mining is an algorithm that, given an event log, produces fitting, precise, properly generalizing and simple process models. While there is consensus on the existence of solid metrics for fitness and simplicity, current metrics for precision and generalization have important flaws, which hamper their applicability in a general setting. In this paper, a novel approach to measure precision and generalization is presented, which relies on the notion of antialignments. An anti-alignment describes highly deviating model traces with respect to observed behavior. We propose metrics for precision and generalization that resemble the leave-one-out cross-validation techniques, where individual traces of the log are removed and the computed anti-alignment assess the model’s capability to describe precisely or generalize the observed behavior. The metrics have been implemented in ProM and tested on several examples.
CITATION STYLE
Van Dongen, B. F., Carmona, J., & Chatain, T. (2016). A unified approach for measuring precision and generalization based on anti-alignments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9850 LNCS, pp. 39–56). Springer Verlag. https://doi.org/10.1007/978-3-319-45348-4_3
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