An Alignment Cost-Based Classification of Log Traces Using Machine-Learning

Mathilde Boltenhagen (1, 2) , Benjamin Chetioui (3) , Laurine Huber (4)

1 MEXICO - Modeling and Exploitation of Interaction and Concurrency
2 LSV - Laboratoire Spécification et Vérification
3 UiB - University of Bergen
4 ORPAILLEUR - Knowledge representation, reasonning

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.

Domaines: Informatique [cs]

Mathilde Boltenhagen, Benjamin Chetioui, Laurine Huber. An Alignment Cost-Based Classification of Log Traces Using Machine-Learning. ML4PM2020 - First International Workshop on Leveraging Machine Learning in Process Mining, Oct 2020, Padua/ Virtual, Italy. ⟨10.1007/978-3-030-72693-5_11⟩. ⟨hal-03134114⟩