A Novel Unsupervised Capacity Identification Approach to Deal With Redundant Criteria in Multicriteria Decision Making Problems
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Artigo
Data de publicação
2024
Periódico
IEEE Transactions on Fuzzy Systems
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0
Autores
Pelegrina G.D.
Duarte L.T.
Duarte L.T.
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© 1993-2012 IEEE.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 paper, 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.
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'current , 2-additive capacities , Choquet integral , Identification approach , Integral parameters , Multi-criteria decision making problems , Multicriteria decision-making , Multicriterion decision makings , Undesirable effects , Unsupervised capacity identification