relation: https://khub.utp.edu.my/scholars/13958/
title: A critical review on adverse effects of concept drift over machine learning classification models
creator: Jameel, S.M.
creator: Hashmani, M.A.
creator: Alhussain, H.
creator: Rehman, M.
creator: Budiman, A.
description: Big Data (BD) is participating in the current computing revolution in a big way. Industries and organizations are utilizing their insights for Business Intelligence using Machine Learning Models (ML-Models). Deep Learning Models (DL-Models) have been proven to be a better selection than Shallow Learning Models (SL-Models). However, the dynamic characteristics of BD introduce many critical issues for DLModels, Concept Drift (CD) is one of them. CD issue frequently appears in Online Supervised Learning environments in which data trends change over time. The problem may even worsen in the BD environment due to veracity and variability factors. Due to the CD issue, the accuracy of classification results degrades in ML-Models, which may make ML-Models not applicable. Therefore, ML-Models need to adapt quickly to changes to maintain the accuracy level of the results. In current solutions, a substantial improvement in accuracy and adaptability is needed to make ML-Models robust in a non-stationary environment. In the existing literature, the consolidated information on this issue is not available. Therefore, in this study, we have carried out a systematic critical literature review to discuss the Concept Drift taxonomy and identify the adverse effects and existing approaches to mitigate CD. © 2013 The Science and Information (SAI) Organization.
publisher: Science and Information Organization
date: 2020
type: Article
type: PeerReviewed
identifier:   Jameel, S.M. and Hashmani, M.A. and Alhussain, H. and Rehman, M. and Budiman, A.  (2020) A critical review on adverse effects of concept drift over machine learning classification models.  International Journal of Advanced Computer Science and Applications, 11 (1).  pp. 206-211.  ISSN 2158107X     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080111380&doi=10.14569%2fijacsa.2020.0110127&partnerID=40&md5=9101934f9d961d84b0e615d544797c26
relation: 10.14569/ijacsa.2020.0110127
identifier: 10.14569/ijacsa.2020.0110127