A semisupervised concept drift adaptation via prototype-based manifold regularization approach with knowledge transfer

A semisupervised concept drift adaptation via prototype-based manifold regularization approach with knowledge transfer

Data stream mining deals with processing large amounts of data in nonstationary environments, where the relationship between the data and the labels often changes. Such dynamic relationships make it difficult to design a computationally efficient data stream processing algorithm that is also adaptab...

Full description

Saved in:
Bibliographic Details
Main Authors: Muhammad Zaly Shah, Muhammad Zafran, Zainal, Anazida, Eisa, Taiseer Abdalla Elfadil, Albasheer, Hashim, Ghaleb, Fuad A.
Format: Article
Language:English
Published: MDPI 2023
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/105665/1/MuhammadZafran2023_ASemisupervisedConceptDriftAdaptation.pdf
http://eprints.utm.my/105665/
http://dx.doi.org/10.3390/math11020355
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Malaysia
Language: English

Internet

http://eprints.utm.my/105665/1/MuhammadZafran2023_ASemisupervisedConceptDriftAdaptation.pdf
http://eprints.utm.my/105665/
http://dx.doi.org/10.3390/math11020355

Similar Items