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Handling Concept Drift for Predictions in Business Process Mining

Baier, Lucas; Reimold, Josua; Kühl, Niklas

Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction quality of models can be largely influenced by this phenomenon. Therefore, concept drift is usually handled by retraining of the model. However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model. Therefore, we systematically analyze different data selection strategies in this work. Subsequently, we instantiate our findings on a use case in process mining which is strongly affected by concept drift. We can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift handling. Furthermore, we depict the effects of the different data selection strategies.

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Preprint §
DOI: 10.5445/IR/1000119266
Veröffentlicht am 13.05.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Karlsruhe Service Research Institute (KSRI)
Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator KITopen-ID: 1000119266
Erschienen in Proceedings of 22nd IEEE International Conference on Business Informatics
Veranstaltung 22nd IEEE International Conference on Business Informatics (2020), Antwerpen, Belgien, 22.06.2020 – 24.06.2020
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