Handling Concept Drift Using the Correlation between Multiple Data Streams

Concept Drift has been a major issue in handing streaming data in machine learning area. To date, the research on concept drift considers data streams separately, ignoring the correlations between data streams. Motivated by this, this research proposes four methods to deal with the correlations between data streams. A concept drift adaptation method is firstly proposed to overcome the insufficient training problem caused by scarce newly arrived data. By introducing correlation between multiple data streams, a multi-stream concept drift handling framework is then proposed to deal with concept drifting for multi-stream environment. Next, Evolutionary Regressor Chains are developed to track the correlations between multiple data streams. And lastly, a concept drift adaptation strategy for neural network classifiers is also developed for circumstances in which multiple data streams have different feature spaces. Extensive experiments have been conducted to evaluate the developed methods.

Author dc:creator

• Zhang, Bin

Statement dc:rights

• info:eu-repo/semantics/openAccess
• The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
• © 2022 Bin Zhang
• au.edu.uts.lib/cph

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10453/172012

OAI identifier oai:identifier

oai:opus.lib.uts.edu.au:10453/172012