A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques
Sign Up to like & getrecommendations! Published in 2024 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"
DOI: 10.1002/widm.1536
Abstract: Last decade demonstrate the massive growth in organizational data which keeps on increasing multi‐fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many… read more here.
Keywords: streaming data; concept; detection adaptation; concept drift ... See more keywords
Continually trained life-long classification
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DOI: 10.1007/s00521-021-06154-9
Abstract: Two challenges can be found in a life-long classifier that learns continually: the concept drift, when the probability distribution of data is changing in time, and catastrophic forgetting when the earlier learned knowledge is lost.… read more here.
Keywords: life; concept drift; catastrophic forgetting; long classification ... See more keywords
Efficient quantile tracking using an oracle
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DOI: 10.1007/s10489-022-03489-1
Abstract: Concept drift is a well-known issue that arises when working with data streams. In this paper, we present a procedure that allows a quantile tracking procedure to cope with concept drift. We suggest using expected… read more here.
Keywords: tracking using; concept drift; efficient quantile; quantile tracking ... See more keywords
Concept drift over geological times: predictive modeling baselines for analyzing the mammalian fossil record
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DOI: 10.1007/s10618-018-0606-6
Abstract: Fossils are the remains organisms from earlier geological periods preserved in sedimentary rock. The global fossil record documents and characterizes the evidence about organisms that existed at different times and places during the Earth’s history.… read more here.
Keywords: concept drift; fossil record; fossil; geological times ... See more keywords
Analyzing and repairing concept drift adaptation in data stream classification
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DOI: 10.1007/s10994-021-05993-w
Abstract: Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model may be able… read more here.
Keywords: drift adaptation; data stream; concept drift; drift ... See more keywords
Large-Scale Stream k-means based on Product-Quantized codes
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DOI: 10.1007/s13042-025-02531-1
Abstract: Data stream clustering (DSC) is one of the most significant and widely studied research directions in the field of data mining. However, for the processing of large-scale data streams, most existing methods suffer from slow… read more here.
Keywords: concept drift; scale stream; stream; product quantized ... See more keywords
Unsupervised concept drift detection based on multi-scale slide windows
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DOI: 10.1016/j.adhoc.2020.102325
Abstract: Abstract In the past few decades, research related to concept drift learning has been increasing, and many concept drift learning algorithms have also been developed and applied to actual data stream processing. In general, concept… read more here.
Keywords: concept drift; drift; drift detection; multi scale ... See more keywords
A taxonomic look at instance-based stream classifiers
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DOI: 10.1016/j.neucom.2018.01.062
Abstract: Abstract Large numbers of data streams are today generated in many fields. A key challenge when learning from such streams is the problem of concept drift. Many methods, including many prototype methods, have been proposed… read more here.
Keywords: concept drift; taxonomic look; based stream; instance based ... See more keywords
Learning in the presence of class imbalance and concept drift
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DOI: 10.1016/j.neucom.2019.01.080
Abstract: Accepted papers will be published in our Workshop proceeding. The authors will be invited to submit an extended version to the special issue published in Neurocomputing or Connection Science (confirmed). With the wide application of… read more here.
Keywords: concept drift; imbalance concept; class imbalance;
Adaptive learning on mobile network traffic data
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DOI: 10.1080/09540091.2018.1512557
Abstract: ABSTRACT Machine learning based mobile traffic classification has become a popular topic in recent years. As mobile traffic data is dynamic in nature, the static model has become ineffective for the task of classifying future… read more here.
Keywords: concept drift; traffic; classification; model ... See more keywords
Learning from data streams and class imbalance
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DOI: 10.1080/09540091.2019.1572975
Abstract: With the wide application of machine learning algorithms to the real world, class imbalanceandconceptdrift havebecomecrucial learning issues. Applications in variousdomains such as riskmanagement, anomaly detection, fraud detection, software engineering, social media mining, and recommender systems… read more here.
Keywords: concept drift; data streams; class; class imbalance ... See more keywords