Concept drift detection and localization framework based on behavior replacement
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-04341-2.pdf
Reference23 articles.
1. Sato DMV, De Freitas SC, Barddal JP, Scalabrin EE (2021) A survey on concept drift in process mining. ACM Computing Surveys (CSUR) 54(9):1–38
2. Bose RPJC, van der Aalst WMP, žliobaitė I, Pechenizkiy M (2011) Handling concept drift in process mining. In: Mouratidis H, Rolland C (eds) Engineering, advanced information systems. Springer, pp 391–405
3. Maaradji A, Dumas M, Rosa ML, Ostovar A (2016) Fast and accurate business process drift detection. In: International conference on business process management. Springer, pp 406–422
4. Stertz F, Rinderle-Ma S (2018) Process histories - detecting and representing concept drifts based on event streams. In: Panetto H, Debruyne C, Proper HA, Ardagna CA, Roman D, Meersman R (eds) On the move to meaningful internet systems. OTM 2018 conferences. Springer, pp 318–335
5. Stertz F, Rinderle-Ma S (2019) Detecting and identifying data drifts in process event streams based on process histories. In: Cappiello C, Ruiz M (eds) Information systems engineering in responsible information systems. Springer, pp 240–252
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