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A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application

Author

Listed:
  • Ioannis Mallidis

    (Department of Statistical and Insurance Science, University of Western Macedonia, 50100 Kozani, Greece)

  • Volha Yakavenka

    (Department of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Anastasios Konstantinidis

    (Department of Accounting and Finance, University of Western Macedonia, 50100 Kozani, Greece)

  • Nikolaos Sariannidis

    (Department of Accounting and Finance, University of Western Macedonia, 50100 Kozani, Greece)

Abstract

The paper develops a goal programming-based multi-criteria methodology, for assessing different machine learning (ML) regression models under accuracy and time efficiency criteria. The developed methodology provides users with high flexibility in assessing the models as it allows for a fast and computationally efficient sensitivity analysis of accuracy and time significance weights as well as accuracy and time significance threshold values. Four regression models were assessed, namely the decision tree, random forest, support vector and the neural network. The developed methodology was employed to forecast the time to failures of NASA Turbofans. The results reveal that decision tree regression (DTR) seems to be preferred for low values of accuracy weights (up to 30%) and low accuracy and time efficiency threshold values. As the accuracy weights tend to increase and for higher accuracy and time efficiency threshold values, random forest regression (RFR) seems to be the best choice. The preference for the RFR model however, seems to change towards the adoption of the neural network for accuracy weights equal to and higher than 90%.

Suggested Citation

  • Ioannis Mallidis & Volha Yakavenka & Anastasios Konstantinidis & Nikolaos Sariannidis, 2021. "A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application," Mathematics, MDPI, vol. 9(19), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2405-:d:644466
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    References listed on IDEAS

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    1. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
    2. Junfei Chen & Ming Li & Weiguang Wang, 2012. "Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-12, September.
    3. Adolfo Crespo Márquez & Antonio de la Fuente Carmona & Sara Antomarioni, 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency," Energies, MDPI, vol. 12(18), pages 1-25, September.
    4. Atanu Sengupta & Sanjoy De, 2020. "Review of Literature," India Studies in Business and Economics, in: Assessing Performance of Banks in India Fifty Years After Nationalization, chapter 0, pages 15-30, Springer.
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