A Three Layer Super Learner Ensemble with Hyperparameter Optimization to Improve the Performance of Machine Learning Model

Authors

  • Sanvitha Kasthuriarachchi University of Kelaniya
  • S. R. Liyanage

DOI:

https://doi.org/10.31357/ait.v1i1.4844

Abstract

A combination of different machine learning models to form a super learner can definitely lead to improved predictions in any domain. The super learner ensemble discussed in this study collates several machine learning models and proposes to enhance the performance by considering the final meta- model accuracy and the prediction duration. An algorithm is proposed to rate the machine learning models derived by combining the base classifiers voted with different weights. The proposed algorithm is named as Log Loss Weighted Super Learner Model (LLWSL). Based on the voted weight, the optimal model is selected and the machine learning method derived is identified. The meta- learner of the super learner uses them by tuning their hyperparameters.  The execution time and the model accuracies were evaluated using two separate datasets inside LMSSLIITD extracted from the educational industry by executing the LLWSL algorithm. According to the outcome of the evaluation process, it has been noticed that there exists a significant improvement in the proposed algorithm LLWSL for use in machine learning tasks for the achievement of better performances.

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Published

2021-05-31

How to Cite

Kasthuriarachchi, S., & Liyanage , S. R. (2021). A Three Layer Super Learner Ensemble with Hyperparameter Optimization to Improve the Performance of Machine Learning Model . Advances in Technology, 1(1), 207–234. https://doi.org/10.31357/ait.v1i1.4844

Issue

Section

Machine Learning/Deep Learning

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