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A Comparative Performance Assessment of Optimized Multilevel Ensemble Learning Model with Existing Classifier Models.

Mukesh Kumar1, Karan Bajaj1, Bhisham Sharma1

  • 1Department of Computer Science & Engineering, Chitkara University School of Engineering and Technology, Chitkara University, Baddi, Himachal Pradesh, India.

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Summary

This study introduces a novel ensemble modeling approach for classification problems, enhancing prediction accuracy by combining multiple algorithms. The proposed method effectively identifies and removes underperforming features, achieving up to 88.3% accuracy in diabetes detection.

Keywords:
AdaBoostclassification algorithmslogistic regressionnaïve Bayespredictionrandom forest

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Science

Background:

  • Traditional classification problems often rely on single predictive models.
  • Ensemble modeling offers a more robust approach by integrating multiple predictive models.
  • Existing ensemble methods can be improved through strategic base classifier selection.

Purpose of the Study:

  • To propose a multilevel approach for selecting base classifiers in ensemble classification.
  • To enhance prediction accuracy by dropping underperforming features and addressing collinearity.
  • To evaluate the proposed ensemble model against existing classification algorithms.

Main Methods:

  • A multilevel strategy for selecting base classifiers was developed.
  • Feature selection focused on removing poor-performing features and collinearity.
  • The proposed model was tested on datasets from the University of California, Irvine repository.
  • Performance was compared against single classifiers and other ensemble methods.

Main Results:

  • The proposed ensemble model achieved classification accuracy between 70% and 88.3%.
  • The novel approach demonstrated superior performance compared to single classifiers like artificial neural networks and Radial Variant Function Neural Network/Fish Swarm Algorithm.
  • The Naive Bayes algorithm showed the lowest accuracy (around 71.9%), while the proposed NB-RF-LR-SEMod achieved a maximum of 88.3% on the PIMA Diabetes dataset.

Conclusions:

  • The developed multilevel ensemble approach significantly improves classification accuracy.
  • This method offers a powerful tool for early disease detection, exemplified by its success in diabetes prediction.
  • The findings suggest that strategic feature selection and classifier combination are key to effective ensemble modeling.