Handling Concept Drift for Predictions in Business Process Mining

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Handling Concept Drift for Predictions in Business Process Mining

12 May 2020

Niklas Kühl

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Papers citing "Handling Concept Drift for Predictions in Business Process Mining"

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Title
Detecting sudden and gradual drifts in business processes from execution traces Abderrahmane Maaradji Marlon Dumas M. Rosa Alireza Ostovar AI4TS 38 80 0 07 May 2020
Machine Learning in Artificial Intelligence: Towards a Common Understanding Niklas Kühl Marc Goutier Robin Hirt G. Satzger AI4CE 56 47 0 27 Mar 2020
Comprehensive Process Drift Detection with Visual Analytics Anton Yeshchenko Claudio Di Ciccio Jan Mendling Artem Polyvyanyy AI4TS 49 47 0 15 Jul 2019
Scikit-Multiflow: A Multi-output Streaming Framework Jacob Montiel Jesse Read Albert Bifet T. Abdessalem 43 307 0 12 Jul 2018
Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring I. Verenich Marlon Dumas M. Rosa F. Maggi Irene Teinemaa AI4TS 63 156 0 08 May 2018
Characterizing Concept Drift Geoffrey I. Webb Roy Hyde Hong Cao Hai-Long Nguyen F. Petitjean 78 424 0 12 Nov 2015
Learning under Concept Drift: an Overview Indrė Žliobaitė 110 456 0 22 Oct 2010