Diverse instance-weighting ensemble based on region drift disagreement for concept drift adaptation

A Liu, J Lu, G Zhang - … on neural networks and learning systems, 2020 - ieeexplore.ieee.org
Concept drift refers to changes in the distribution of underlying data and is an inherent
property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …
被引用次数：60 相关文章

Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation.

A Liu, J Lu, G Zhang - IEEE transactions on neural networks and …, 2021 - opus.lib.uts.edu.au
Concept drift refers to changes in the distribution of underlying data and is an inherent
property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …

Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation

A Liu, J Lu, G Zhang - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
Abstract Concept drift refers to changes in the distribution of underlying data and is an
inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has …

Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation.

A Liu, J Lu, G Zhang - IEEE Transactions on Neural Networks and …, 2021 - europepmc.org
Concept drift refers to changes in the distribution of underlying data and is an inherent
property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …

Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation

A Liu, J Lu, G Zhang - IEEE transactions on neural …, 2021 - pubmed.ncbi.nlm.nih.gov
Concept drift refers to changes in the distribution of underlying data and is an inherent
property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …

Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation

A Liu, J Lu, G Zhang - arXiv preprint arXiv:2004.05810, 2020 - arxiv.org
Concept drift refers to changes in the distribution of underlying data and is an inherent
property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …