Semi-Supervised Discovery of DNN-Based Outcome Predictors from Scarcely-Labeled Process Logs

Francesco Folino (), Gianluigi Folino (), Massimo Guarascio () and Luigi Pontieri ()
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Francesco Folino: ICAR-CNR
Gianluigi Folino: ICAR-CNR
Massimo Guarascio: ICAR-CNR
Luigi Pontieri: ICAR-CNR

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2022, vol. 64, issue 6, No 3, 729-749

Abstract: Abstract Predicting the final outcome of an ongoing process instance is a key problem in many real-life contexts. This problem has been addressed mainly by discovering a prediction model by using traditional machine learning methods and, more recently, deep learning methods, exploiting the supervision coming from outcome-class labels associated with historical log traces. However, a supervised learning strategy is unsuitable for important application scenarios where the outcome labels are known only for a small fraction of log traces. In order to address these challenging scenarios, a semi-supervised learning approach is proposed here, which leverages a multi-target DNN model supporting both outcome prediction and the additional auxiliary task of next-activity prediction. The latter task helps the DNN model avoid spurious trace embeddings and overfitting behaviors. In extensive experimentation, this approach is shown to outperform both fully-supervised and semi-supervised discovery methods using similar DNN architectures across different real-life datasets and label-scarce settings.

Keywords: Process mining; Outcome prediction; Deep learning; Semi-supervised learning (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12599-022-00749-9

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