Tuning Machine Learning to Address Process Mining Requirements
Paolo Ceravolo,
Sylvio Barbon Junior,
Ernesto Damiani
et al.
Abstract:Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. Often, the design of such models is based on some ad-hoc assumptions about the corresponding data distributions, which are not necessarily in accordance with the non-parametric distributions typically observed with process data. Moreover, the learning procedure they follow ignores the constraints concurrency imposes on pr… Show more
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