Abstract
Conformance checking is an important aspect of process mining that identifies the differences between the behaviors recorded in a log and those exhibited by an associated process model. Machine learning and deep learning methods perform extremely well in sequence analysis. We successfully apply both a Recurrent Neural Network and a Random Forest classifiers to the problem of evaluating whether the alignment cost of a log trace to a process model is below an arbitrary threshold, and provide a lower bound for the fitness of the process model based on the classification.
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Notes
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The size of the embedding layer, the number of epochs, the batch size, and the stack of LSTM layers were chosen after several initial experiments, as they were the parameters that yielded the best results.
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Boltenhagen, M., Chetioui, B., Huber, L. (2021). An Alignment Cost-Based Classification of Log Traces Using Machine-Learning. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_11
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