Genetic algorithms for hyperparameter optimization in predictive business process monitoring

Genetic algorithms for hyperparameter optimization in predictive business process monitoring Predictive business process monitoring aims at predicting the outcome of ongoing cases of a business process based on past execution traces. A wide range of techniques for this predictive task have been proposed in the literature. It turns out that no single technique, under a default configuration, consistently achieves the best predictive accuracy across all datasets. Thus, the selection and configuration of a technique needs to be done for each dataset. This paper presents a framework for predictive process monitoring that brings together a range of techniques, each with an associated set of hyperparameters. The framework incorporates two automatic hyperparameter optimization algorithms, which, given a dataset, select suitable techniques for each step in the framework and configure these techniques with minimal user input. The proposed framework and hyperparameter optimization algorithms have been evaluated on two real-life datasets and compared with state-of-the-art approaches for predictive business process monitoring. The results demonstrate the scalability of the approach and its ability to identify accurate and reliable framework configurations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Information Systems Elsevier

Genetic algorithms for hyperparameter optimization in predictive business process monitoring

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0306-4379
D.O.I.
10.1016/j.is.2018.01.003
Publisher site
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Abstract

Predictive business process monitoring aims at predicting the outcome of ongoing cases of a business process based on past execution traces. A wide range of techniques for this predictive task have been proposed in the literature. It turns out that no single technique, under a default configuration, consistently achieves the best predictive accuracy across all datasets. Thus, the selection and configuration of a technique needs to be done for each dataset. This paper presents a framework for predictive process monitoring that brings together a range of techniques, each with an associated set of hyperparameters. The framework incorporates two automatic hyperparameter optimization algorithms, which, given a dataset, select suitable techniques for each step in the framework and configure these techniques with minimal user input. The proposed framework and hyperparameter optimization algorithms have been evaluated on two real-life datasets and compared with state-of-the-art approaches for predictive business process monitoring. The results demonstrate the scalability of the approach and its ability to identify accurate and reliable framework configurations.

Journal

Information SystemsElsevier

Published: May 1, 2018

References

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