Integrating feature selection and fuzzy decision-making: A spherical triangular fuzzy number based framework for large-scale decision-making

This study introduces a novel Fuzzy Large-Scale Decision-Making (FLSDM) framework designed to address the complexities of managing a large number of criteria in fuzzy decision-making contexts. While Multi-criteria decision-making (MCDM) methods are widely used across disciplines, traditional approaches often struggle when confronted with high-dimensional decision parameters. To overcome this, we propose an integrated feature selection algorithm that integrates machine learning (ML) algorithms, namely Extreme Gradient Boosting (XGBoost), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and ReliefF, within a triangular spherical fuzzy (STFN) environment to select the core criteria from a large dataset. Additionally, we extend the Integrated Determination of Objective Criteria Weights (IDOCRIW) and Additive Ratio Assessment (ARAS) methods for the STFN environment to calculate criteria weights and rank alternatives under fuzziness, respectively. The application of the proposed framework is demonstrated through a case study of ranking 10 sustainable energy sources based on a comprehensive set of sustainability indicators, including economic, technical, social, environmental, and political dimensions. Extensive robustness and sensitivity analyses validate the model’s effectiveness in managing complex, large-scale decision scenarios.