A comprehensive active learning method for multiclass imbalanced data streams with concept drift

A challenge to many real-world applications is multiclass imbalance with concept drift. In this paper, we propose a comprehensive active learning method for multiclass imbalanced streaming data with concept drift (CALMID). First, we design a comprehensive online active learning framework that includ...

Full description

Saved in:

Bibliographic Details
Published in:	Knowledge-based systems Vol. 215; p. 106778
Main Authors:	Liu, Weike, Zhang, Hang, Ding, Zhaoyun, Liu, Qingbao, Zhu, Cheng
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 05.03.2021 Elsevier Science Ltd
Subjects:	Active learning Algorithms Classifiers Concept drift Data stream Data transmission Drift Imbalance Learning Machine learning Multiclass imbalance Online active learning Sliding Teaching methods Thresholds Uncertainty Multiclass imbalance Data stream Online active learning Concept drift
ISSN:	0950-7051, 1872-7409
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!