Detecting concept drift in data streams using model explanation-Reference-Cited by-同舟云学术

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Detecting concept drift in data streams using model explanation

Published:2018-02 Issue: Volume:92 Page:546-559
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Demšar Jaka,Bosnić Zoran

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference80 articles.

1. The internet of things: A survey from the data-centric perspective;Aggarwal,2013

2. Evolving intelligent systems: Methodology and applications;Angelov,2010

3. Improved concept drift handling in surgery prediction and other applications;Beyene;Knowledge and Information Systems,2014

4. Learning from time-changing data with adaptive windowing;Bifet,2007

5. Adaptive learning from evolving data streams;Bifet,2009

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1. Thwarting Cybersecurity Attacks with Explainable Concept Drift;2024 International Wireless Communications and Mobile Computing (IWCMC);2024-05-27

2. Drift Detection for Black-Box Deep Learning Models;IT Professional;2024-03

3. Interpretation Conclusion Stability of Software Defect Prediction over Time;2024-03-01

4. LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams;2024-02-01

5. Interpretable Model Drift Detection;Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD);2024-01-04

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