Results 41 to 50 of about 5,397,718 (323)
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.
Wang, Heng, Abraham, Zubin
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Combining Diverse Meta-Features to Accurately Identify Recurring Concept Drift in Data Streams
ACM Transactions on Knowledge Discovery from Data, 2023 Learning from streaming data is challenging as the distribution of incoming data may change over time, a phenomenon known as concept drift. The predictive patterns, or experience learned under one distribution may become irrelevant as conditions change ...
B. Halstead +4 more
semanticscholar +1 more source
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.
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 It makes well-trained static learning models lose accuracy and become outdated as time goes by.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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Anomaly and change point detection for time series with concept drift
World wide web (Bussum), 2023 Anomaly detection is one of the most important research contents in time series data analysis, which is widely used in many fields. In real world, the environment is usually dynamically changing, and the distribution of data changes over time, namely ...
Jiayi Liu +4 more
semanticscholar +1 more source
PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams [PDF]
Global Communications Conference, 2021 As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT
Li Yang, D. Manias, A. Shami
semanticscholar +1 more source
Adaptive Incremental-Learning Ensemble Classification Approach for Concept Drift Problem
Jisuanji kexue yu tansuo, 2020 The performance of the machine learning model always decreases with the occurrence of concept drift due to the non-stationary characteristics of the data flow.
HAN Mingming, SUN Guanglu, ZHU Suxia
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Robust textual data streams mining based on continuous transfer learning [PDF]
, 2013 Copyright © SIAM. In textual data stream environment, concept drift can occur at any time, existing approaches partitioning streams into chunks can have problem if the chunk boundary does not coincide with the change point which is impossible to predict.
Cao, L, Hao, Z, Liu, B, Xiao, Y, Yu, PS
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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 ...
Xu, Yunwen +3 more
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Learning in the presence of concept recurrence in data stream clustering
Journal of Big Data, 2020 In the case of real-world data streams, the underlying data distribution will not be static; it is subject to variation over time, which is known as the primary reason for concept drift.
K. Namitha, G. Santhosh Kumar
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