This paper introduces a method for characterizing Fuzzy Integral (FI) based aggregation by focusing on the parametrization of Fuzzy Measures (FM). The research highlights that densities alone are insufficient to uniquely identify a discrete FM, but an interval-valued FM can be uniquely determined. By incorporating additional information such as a specific FI and a dataset, more specific interval-valued FMs can be obtained. The paper also proposes a way to determine the likelihood of an empirical FM encompassing the ideal FM, providing a confidence interval for FI fusion outcomes. AI
IMPACT This research offers a novel method for characterizing information fusion outcomes, potentially improving the reliability and interpretability of ensemble and decision-level fusion systems in AI.
RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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