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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
DOI: 10.1109/CBI49978.2020.00016
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator ISBN: 978-1-72819-927-6
KITopen-ID: 1000119266
Erschienen in 2020 IEEE 22nd Conference on Business Informatics : CBI 2020 : Antwerp, Belgium, 22-24 June 2020 : proceedings. Vol. 1
Veranstaltung 22nd IEEE Conference on Business Informatics (CBI 2020), Antwerpen, Belgien, 22.06.2020 – 24.06.2020
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 76-83
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