eprintid: 20178
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/02/01/78
datestamp: 2024-06-04 14:19:55
lastmod: 2024-06-04 14:19:55
status_changed: 2024-06-04 14:16:46
type: article
metadata_visibility: show
creators_name: Palli, A.S.
creators_name: JafreezalJaafar, 
creators_name: Gilal, A.R.
creators_name: Alsughayyir, A.
creators_name: Gomes, H.M.
creators_name: Alshanqiti, A.
creators_name: Omar, M.
title: Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review
ispublished: pub
note: cited By 0
abstract: In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for concept detection, a separate mechanism is added in parallel with the classifier to detect the concept drift called a drift detector. For concept adaptation, the classifier updates itself or trains a new classifier to replace the older one. In case, the data stream faces a class imbalance issue, the classifier may not properly adapt to the latest concept. In this survey, we study how the existing work addresses the issues of class imbalance and concept drift while learning from non-stationary data streams. We further highlight the limitation of existing work and challenges caused by other factors of class imbalance along with concept drift in data stream classification. Results of our survey found that, out of 1110 studies, by using our inclusion and exclusion criteria, we were able to narrow the pool of articles down to 35 that directly addressed our study objectives. The study found that issues such as multiple concept drift types, dynamic class imbalance ratio, and multi-class imbalance in presence of concept drift are still open for further research. We also observed that, while major research efforts have been dedicated to resolving concept drift and class imbalance, not much attention has been given to with-in-class imbalance, rear examples, and borderline instances when they exist with concept drift in multi-class data. This paper concludes with some suggested future directions. © (2023), (Universiti Utara Malaysia Press). All Rights Reserved.
date: 2024
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184775410&doi=10.32890%2fjict2024.23.1.5&partnerID=40&md5=21de181ad06bb87225976ef05e6c42b4
id_number: 10.32890/jict2024.23.1.5
full_text_status: none
publication: Journal of Information and Communication Technology
volume: 23
number: 1
pagerange: 105-139
refereed: TRUE
citation:   Palli, A.S. and JafreezalJaafar and Gilal, A.R. and Alsughayyir, A. and Gomes, H.M. and Alshanqiti, A. and Omar, M.  (2024) Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review.  Journal of Information and Communication Technology, 23 (1).  pp. 105-139.