Results 41 to 50 of about 3,595,676 (296)
A Survey on Concept Drift in Process Mining [PDF]
ACM Computing Surveys, 2021 Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and ...
Denise Maria Vecino Sato +3 more
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Active Fuzzy Weighting Ensemble for Dealing with Concept Drift
International Journal of Computational Intelligence Systems, 2018 The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by.
Fan Dong +3 more
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Analysis of Descriptors of Concept Drift and Their Impacts
InformaticsConcept drift, a phenomenon that can lead to degradation of classifier performance over time, is commonly addressed in the literature through detection and reaction strategies.
Albert Costa +2 more
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Concept drift learning with alternating learners [PDF]
2017 International Joint Conference on Neural Networks (IJCNN), 2017 Data-driven predictive analytics are in use today across a number of industrial applications, but further integration is hindered by the requirement of similarity among model training and test data distributions. This paper addresses the need of learning from possibly nonstationary data streams, or under concept drift, a commonly seen phenomenon in ...
Yunwen Xu +3 more
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Concept Drift Detection Based on Deep Neural Networks and Autoencoders
Applied SciencesIn domains such as fraud detection, healthcare, and industrial equipment maintenance, streaming data often exhibit characteristics such as continuous generation, high real-time processing requirements, and complex distributions, making it susceptible to ...
Lisha Hu, Yaru Lu, Yuehua Feng
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Predictive learning models for concept drift [PDF]
Theoretical Computer Science, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
John Case +4 more
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Unsupervised Tuning for Drift Detectors Using Change Detector Segmentation
IEEE AccessConcept drifts can occur due to various factors such as changes in the environment or sensor degradation, posing significant challenges to machine learning systems by potentially skewing decision-making processes. Therefore, detecting drifts is essential
Ricardo Petri Silva +3 more
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Concept drift estimation with graphical models
Information Sciences, 2022 This paper deals with the issue of concept-drift in machine learning in the context of high dimensional problems. In contrast to previous concept drift detection methods, this application does not depend on the machine learning model in use for a specific target variable, but rather, it attempts to assess the concept drift as an independent ...
Riso, Luigi, Guerzoni, Marco
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Learning with a Drifting Target Concept [PDF]
, 2015 We study the problem of learning in the presence of a drifting target concept. Specifically, we provide bounds on the error rate at a given time, given a learner with access to a history of independent samples labeled according to a target concept that can change on each round.
Steve Hanneke +2 more
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Concept drift detection for streaming data [PDF]
2015 International Joint Conference on Neural Networks (IJCNN), 2015 Common statistical prediction models often require and assume stationarity in the data. However, in many practical applications, changes in the relationship of the response and predictor variables are regularly observed over time, resulting in the deterioration of the predictive performance of these models.
Heng Wang, Zubin Abraham
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