Meta-ADD: A meta-learning based pre-trained model for concept drift active detection

Hang Yu; Qingyong Zhang; Tianyu Liu; Jie Lü; Yimin Wen; Guangquan Zhang

doi:10.1016/j.ins.2022.07.022

ScienceGate Book Chapters

JOURNAL ARTICLE

Meta-ADD: A meta-learning based pre-trained model for concept drift active detection

Hang Yu Qingyong Zhang Tianyu Liu Jie Lü Yimin Wen Guangquan Zhang

Year: 2022 Journal: Information Sciences Vol: 608 Pages: 996-1009 Publisher: Elsevier BV

DOI: 10.1016/j.ins.2022.07.022

Get Full-Text PDF Get Analytical Report

Keywords:

Concept drift Computer science Meta learning (computer science) Data stream Artificial intelligence Statistical hypothesis testing Detector Data stream mining Machine learning Artificial neural network Test data Data mining Pattern recognition (psychology) Statistics Task (project management) Mathematics

Metrics

Cited By

16.84

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Data Stream Mining Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning and Data Classification

Physical Sciences → Computer Science → Artificial Intelligence

Meta-ADD: A meta-learning based pre-trained model for concept drift active detection

Metrics

Citation History

Topics

Related Documents

Model Performance Prediction: A Meta-Learning Approach for Concept Drift Detection

A META-LEARNING METHOD FOR CONCEPT DRIFT

Towards the Necessity of Pre-trained Heads in Model-Agnostic Meta-Learning

Efficient Concept Drift Detection: A Meta Feature Selection Approach

Meta Distant Transfer Learning for Pre-trained Language Models