Recognizing input space and target concept drifts in data streams with scarcely labeled and unlabelled instances

Title
Recognizing input space and target concept drifts in data streams with scarcely labeled and unlabelled instances
Authors
Keywords
Data stream classification, Input space and target concept drift, Drift detection, Scarcely labeled and unlabeled streams, Semi-supervised and unsupervised performance indicators, Single-pass active learning filter
Journal
INFORMATION SCIENCES
Volume 355-356, Issue -, Pages 127-151
Publisher
Elsevier BV
Online
2016-03-24
DOI
10.1016/j.ins.2016.03.034

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