TY  - JOUR
AV  - none
N1  - cited By 7
TI  - A critical review on adverse effects of concept drift over machine learning classification models
SP  - 206
PB  - Science and Information Organization
SN  - 2158107X
EP  - 211
IS  - 1
N2  - 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.
ID  - scholars13958
KW  - Computer aided instruction; Deep learning; E-learning; Knowledge acquisition; Neural networks; Supervised learning
KW  -  Adaptive convolutional neural network extreme learning machine; Big data classification; Concept drift; Concept drifts; Convolutional neural network; Data classification; Deep learning; Hybrid drift; Meta-cognitive online sequential extreme learning machine; Metacognitives; Online sequential extreme learning machine; Online supervised learning; Real drift; Shallow learning; Virtual drift
KW  -  Big data
Y1  - 2020///
A1  - Jameel, S.M.
A1  - Hashmani, M.A.
A1  - Alhussain, H.
A1  - Rehman, M.
A1  - Budiman, A.
JF  - International Journal of Advanced Computer Science and Applications
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080111380&doi=10.14569%2fijacsa.2020.0110127&partnerID=40&md5=9101934f9d961d84b0e615d544797c26
VL  - 11
ER  -