Results 21 to 30 of about 3,595,676 (296)
Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions [PDF]
Transactions on Fuzzy Sets and Systems, 2022 People often change their minds at different times and at different places. It is important and valuable to indicate concept-drift patterns in unexpected ways for shopping behaviours for commercial applications. Research about concept drift has been
Tzung-Pei Hong +3 more
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Characterizing concept drift [PDF]
Data Mining and Knowledge Discovery, 2016 Accepted for publication in Data Mining and Knowledge ...
Geoffrey I. Webb +4 more
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Streaming Data Classification Based on Hierarchical Concept Drift and Online Ensemble
IEEE Access, 2023 In order to improve the performance of online learning in the real-time distribution of streaming data, a streaming data classification algorithm based on hierarchical concept drift and online ensemble(SCHCDOE) is proposed in this paper.
Ning Liu, Jianhua Zhao
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Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
Applied Sciences, 2023 With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become ...
Qiuyan Xiang +3 more
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Adaptive Concept Drift Detection [PDF]
Proceedings of the 2009 SIAM International Conference on Data Mining, 2009 AbstractAn established method to detect concept drift in data streams is to perform statistical hypothesis testing on the multivariate data in the stream. The statistical theory offers rank‐based statistics for this task. However, these statistics depend on a fixed set of characteristics of the underlying distribution. Thus, they work well whenever the
Dries, Anton, Rückert, Ulrich
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Concept Drift Adaptation with Incremental–Decremental SVM
Applied Sciences, 2021 Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier.
Honorius Gâlmeanu, Răzvan Andonie
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Concept Drift Data Stream Classification Algorithm Based on McDiarmid Bound
Jisuanji kexue yu tansuo, 2021 Concept drift in data streams can cause significant performance degradation of existing classification models. Most current data stream algorithms for concept drift only aim at a certain type of concept drift (such as abrupt, gradual, or recurring drift),
LIANG Bin, LI Guanghui
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Boosting classifiers for drifting concepts [PDF]
Intelligent Data Analysis, 2007 In many real-world classification tasks, data arrives over time and the target concept to be learned from the data stream may change over time. Boosting methods are well-suited for learning from data streams, but do not address this concept drift problem.
Scholz, Martin, Klinkenberg, Ralf
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Detecting and Responding to Concept Drift in Business Processes
Algorithms, 2022 Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is a proper representation of the new data.
Lingkai Yang +4 more
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Mining frequent itemsets from streaming transaction data using genetic algorithms
Journal of Big Data, 2020 This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine.
Sikha Bagui, Patrick Stanley
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