A critical review on adverse effects of concept drift over machine learning classification models

Syed Muslim Jameel, Manzoor Ahmed Hashmani, Hitham Alhussain, Mobashar Rehman, Arif Budiman

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)206-211
Number of pages6
JournalInternational Journal of Advanced Computer Science and Applications
Issue number1
Publication statusPublished - 2020


  • Adaptive convolutional neural network extreme learning machine (ACNNELM)
  • Big data classification
  • Concept drift
  • Concept drift (CD)
  • Deep learning (DL)
  • Hybrid drift (HD)
  • Machine learning
  • Meta-cognitive online sequential extreme learning machine (MOSELM)
  • Online sequential extreme learning machine (OSELM)
  • Online supervised learning
  • Real drift (RD)
  • Shallow learning (SL)
  • Virtual drift (VD)


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