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A novel unsupervised capacity identification approach to deal with redundant criteria in multicriteria decision making problems
  • Guilherme Dean Pelegrina,
  • Leonardo Tomazeli Duarte
Guilherme Dean Pelegrina

Corresponding Author:[email protected]

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Leonardo Tomazeli Duarte


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.
10 Feb 2024Submitted to TechRxiv
14 Feb 2024Published in TechRxiv