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A comparative study of the leading machine learning techniques and two new optimization algorithms

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  • Baumann, P.
  • Hochbaum, D.S.
  • Yang, Y.T.

Abstract

We present here a computational study comparing the performance of leading machine learning techniques to that of recently developed graph-based combinatorial optimization algorithms (SNC and KSNC). The surprising result of this study is that SNC and KSNC consistently show the best or close to best performance in terms of their F1-scores, accuracy, and recall. Furthermore, the performance of SNC and KSNC is considerably more robust than that of the other algorithms; the others may perform well on average but tend to vary greatly across data sets. This demonstrates that combinatorial optimization techniques can be competitive as compared to state-of-the-art machine learning techniques. The code developed for SNC and KSNC is publicly available.

Suggested Citation

  • Baumann, P. & Hochbaum, D.S. & Yang, Y.T., 2019. "A comparative study of the leading machine learning techniques and two new optimization algorithms," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1041-1057.
    • Handle: RePEc:eee:ejores:v:272:y:2019:i:3:p:1041-1057
    DOI: 10.1016/j.ejor.2018.07.009
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    References listed on IDEAS

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    Cited by:

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    3. Corrado Coppola & Lorenzo Papa & Marco Boresta & Irene Amerini & Laura Palagi, 2024. "Tuning parameters of deep neural network training algorithms pays off: a computational study," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 579-620, October.
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    5. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.

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