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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
A Critical Review on Adverse Effects of Concept Drift over Machine Learning Classification Models
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110127
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.
Abstract: 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 DL-Models, 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.
Keywords: Big data classification; machine learning; online supervised learning; concept drift; Adaptive Convolutional Neural Network Extreme Learning Machine (ACNNELM); Meta-Cognitive Online Sequential Extreme Learning Machine (MOSELM); Online Sequential Extreme Learning Machine (OSELM); Real Drift (RD); Virtual Drift (VD); Hybrid Drift (HD); Deep Learning (DL); Shallow Learning (SL); Concept Drift (CD)
Syed Muslim Jameel, Manzoor Ahmed Hashmani, Hitham Alhussain, Mobashar Rehman and Arif Budiman, “A Critical Review on Adverse Effects of Concept Drift over Machine Learning Classification Models” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110127
@article{Jameel2020,
title = {A Critical Review on Adverse Effects of Concept Drift over Machine Learning Classification Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110127},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110127},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Syed Muslim Jameel and Manzoor Ahmed Hashmani and Hitham Alhussain and Mobashar Rehman and Arif Budiman}
}
