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

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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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Dates and versions

hal-03134114 , version 1 (08-02-2021)

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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⟩
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