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Related Experiment Videos

Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble.

Martin Sarnovsky1, Michal Kolarik1

  • 1Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University in Kosice, Kosice, Slovakia.

Peerj. Computer Science
|April 9, 2021
PubMed
Summary

This study introduces a novel heterogeneous adaptive ensemble model for classifying data streams, effectively handling concept drift. The model balances member performance and diversity for improved predictive accuracy in dynamic environments.

Keywords:
Adaptive ensembleConcept driftData streamsEnsemble learning

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Area of Science:

  • Machine Learning
  • Data Mining
  • Artificial Intelligence

Background:

  • Data streams are dynamic, with changing structures leading to concept drift.
  • Traditional models fail with concept drift; adaptive models are necessary.
  • Adaptive ensemble models are effective for drifting data stream classification.

Purpose of the Study:

  • To present a heterogeneous adaptive ensemble model for data stream classification.
  • To incorporate dynamic class weighting and maintain ensemble diversity.
  • To design a model with diverse base learners (Naive Bayes, k-NN, Decision Trees).

Main Methods:

  • Developed a heterogeneous adaptive ensemble model.
  • Implemented dynamic class weighting and a diversity maintenance mechanism.
  • Utilized Naive Bayes, k-NN, and Decision Trees as base learners.

Main Results:

  • The proposed model demonstrated effective handling of concept drift.
  • Evaluated performance on real-world and synthetic datasets.
  • Compared against existing adaptive ensemble methods.

Conclusions:

  • The heterogeneous adaptive ensemble model shows promise for data stream classification.
  • The model's adaptive mechanisms and diversity maintenance are key to its performance.
  • Further evaluation considered predictive performance and computational resources.