TY  - CONF
A1  - Ghani, N.L.A.
A1  - Aziz, I.A.
A1  - Mehat, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099684409&doi=10.1109%2fICBDA50157.2020.9289802&partnerID=40&md5=6b7cf6bdd29382114490b732a8f4ad74
EP  - 65
Y1  - 2020///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781728192468
N1  - cited By 9; Conference of 2020 IEEE Conference on Big Data and Analytics, ICBDA 2020 ; Conference Date: 17 November 2020 Through 19 November 2020; Conference Code:165892
N2  - Dynamic data streams applications are bound to potential changes in data distribution, of which in the context of data stream mining, will cause concept drift. Data stream mining model must have the capability to adapt to concept drift, otherwise, risk the deterioration of its learning performance. Most of the existing surveys on concept drift detection methods focused on labeled data streams, that may be inapplicable to scenario where true labels are unavailable. The aim of this systematic literature review is to study the existing concept drift detection methods on unlabeled data streams, focusing on the learning process and the way concept drift is monitored in the data stream mining model. A total of 15 articles are selected for final analysis, and it is found that most of the drift detection methods were applied in a supervised learning setting. An experimental evaluation of the methods can be performed in the future work to investigate their performance in unsupervised learning setting. © 2020 IEEE.
ID  - scholars12482
SP  - 61
TI  - Concept Drift Detection on Unlabeled Data Streams: A Systematic Literature Review
KW  - Advanced Analytics; Big data; Data mining; Deterioration; Learning systems; Supervised learning
KW  -  Data distribution; Data stream mining; Detection methods; Experimental evaluation; Learning performance; Learning process; Potential change; Systematic literature review
KW  -  Data streams
AV  - none
ER  -