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Heterogeneous versus Homogeneous Machine Learning Ensembles

Author

Listed:
  • Petrakova Aleksandra
  • Merkurjeva Galina

    (Riga Technical University)

  • Affenzeller Michael

    (Upper Austria University of Applied Sciences)

Abstract

The research demonstrates efficiency of the heterogeneous model ensemble application for a cancer diagnostic procedure. Machine learning methods used for the ensemble model training are neural networks, random forest, support vector machine and offspring selection genetic algorithm. Training of models and the ensemble design is performed by means of HeuristicLab software. The data used in the research have been provided by the General Hospital of Linz, Austria.

Suggested Citation

  • Petrakova Aleksandra & Merkurjeva Galina & Affenzeller Michael, 2015. "Heterogeneous versus Homogeneous Machine Learning Ensembles," Information Technology and Management Science, Sciendo, vol. 18(1), pages 135-140, December.
    • Handle: RePEc:vrs:itmasc:v:18:y:2015:i:1:p:135-140:n:21
    DOI: 10.1515/itms-2015-0021
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    References listed on IDEAS

    as
    1. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
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