A Novel Unsupervised Capacity Identification Approach to Deal With Redundant Criteria in Multicriteria Decision Making Problems

Author(s)

Pelegrina, Guilherme Dean ; Duarte, Leonardo Tomazeli

• Document type: Journal article
• Source: IEEE TRANSACTIONS ON FUZZY SYSTEMS; v. 32, n. 12, p. 6-pg., 2024-12-01.

Abstract

The use of the Choquet integral in multicriteria decision making problems has gained attention in the last two decades. Despite of its usefulness, there is the issue of how to define the Choquet integral parameters, called capacity coefficients, specially the ones associated with coalitions of criteria. A possible approach to address this issue is based on unsupervised learning, which aims to define such parameters with the goal of mitigating undesirable effects provided by intercriteria relations. However, current unsupervised approaches present some drawbacks, as there is no guarantee that the parameters are equally prioritized in the learning procedure. In this article, we propose a novel unsupervised capacity identification approach which ensures a fair learning for all parameters. Moreover, in comparison with the existing methods, our proposal is less complex in terms of optimization, as it is based on a linear formulation. Experimental results in both synthetic and real datasets attest the applicability of our proposal. (AU)

• FAPESP's process: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Grantee: João Marcos Travassos Romano
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• FAPESP's process: 21/11086-0 - Interpretability and fairness in machine learning: Capacity-based functions and interaction indices
• Grantee: Guilherme Dean Pelegrina
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• FAPESP's process: 20/10572-5 - Novel approaches for fairness and transparency in machine learning problems
• Grantee: Guilherme Dean Pelegrina
• Support Opportunities: Scholarships in Brazil - Post-Doctoral