Document Type : Research Paper
Authors
1 School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2 Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke 28372, NC, USA
Abstract
Extracting meaningful information from high-volatility data and uncovering multi-scale knowledge from stable-state data remain key challenges in complex multi-attribute decision-making (MADM) problems. To address these challenges, a novel methodology that integrates stochastic empirical mode decomposition (EMD) with the Choquet integral is proposed. The resulting three-stage framework first decomposes the original data into trend terms, reflecting objective laws, and deviation terms, capturing subjective cognition. These components are then aggregated using Choquet integrals with Shapley values to explicitly model interactions among attributes. Finally, the framework is extended to accommodate four decision scenarios involving known or unknown attribute sets and complete or incomplete attribute values, with regularization introduced to mitigate potential bias. Case studies in investment decision-making demonstrate the effectiveness of the proposed method in integrating objective trends with subjective deviations, highlighting its advantages in multi-attribute information fusion and adaptability to complex decision environments.
Keywords
- Multi-attribute decision-making (MADM)
- Empirical mode decomposition (EMD)
- Choquet integral
- Data mining
Main Subjects
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