Abstract
The commonness of information systems based on machine learning (ML) models and multi-criteria decision analysis (MCDA) methods is growing due to the increasing dimensions of data required for processing. This paper presents a hybrid framework combining the MCDA method with ML models to predict the rankings of countries considering the fulfillment of Sustainable Development Goal 7 based on the identified preferences of decision-makers. The results proved that the proposed approach can be regarded as a functional tool for multi-criteria assessment in the case of inaccessibility of experts' knowledge. The proposed approach enables to mitigate the shortcomings of MCDA methods arising from the necessity to engage decision-makers.
Paper Type
Poster
DOI
10.62036/ISD.2024.79
New Concept to Multi-Criteria Model Automatization - Machine Learning Based Approach
The commonness of information systems based on machine learning (ML) models and multi-criteria decision analysis (MCDA) methods is growing due to the increasing dimensions of data required for processing. This paper presents a hybrid framework combining the MCDA method with ML models to predict the rankings of countries considering the fulfillment of Sustainable Development Goal 7 based on the identified preferences of decision-makers. The results proved that the proposed approach can be regarded as a functional tool for multi-criteria assessment in the case of inaccessibility of experts' knowledge. The proposed approach enables to mitigate the shortcomings of MCDA methods arising from the necessity to engage decision-makers.
Recommended Citation
Wątróbski, J., Bączkiewicz, A. & Rudawska, I. (2024). New Concept to Multi-Criteria Model Automatization - Machine Learning Based Approach. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.79