Buscar
Mostrando ítems 1-8 de 8
Some preference involved aggregation models for basic uncertain information using uncertainty transformation
(IOS Press, 2020)
Artículo / Artikulua,
In decision making, very often the data collected are with different extents of uncertainty. The recently introduced concept, Basic Uncertain Information (BUI), serves as one ideal information representation to well model ...
Dissimilarity based choquet integrals
(Springer, 2020)
info:eu-repo/semantics/conferenceObject,
In this paper, in order to generalize the Choquet integral, we replace the difference between inputs in its definition by a restricted dissimilarity function and refer to the obtained function as d-Choquet integral. For ...
Discrete IV dG-Choquet integrals with respect to admissible orders
(Elsevier, 2021)
info:eu-repo/semantics/article,
In this work, we introduce the notion of dG-Choquet integral, which generalizes the discrete Choquet integral replacing, in the first place, the difference between inputs represented by closed subintervals of the unit ...
A supervised fuzzy measure learning algorithm for combining classifiers
(Elsevier, 2023)
Artículo / Artikulua,
Fuzzy measure-based aggregations allow taking interactions among coalitions of the input sources into account. Their main drawback when applying them in real-world problems, such as combining classifier ensembles, is how ...
d-Choquet integrals: Choquet integrals based on dissimilarities
(Elsevier, 2020)
info:eu-repo/semantics/article,
The paper introduces a new class of functions from [0,1]n to [0,n] called d-Choquet integrals. These functions are a generalization of the 'standard' Choquet integral obtained by replacing the difference in the definition ...
Some bipolar-preferences-involved aggregation methods for a sequence of OWA weight vectors
(Springer, 2021)
info:eu-repo/semantics/article,
The ordered weighted averaging (OWA) operator and its associated weight vectors have been both theoretically and practically verified to be powerful and effective in modeling the optimism/pessimism preference of decision ...
An empirical study on supervised and unsupervised fuzzy measure construction methods in highly imbalanced classification
(IEEE, 2020)
info:eu-repo/semantics/conferenceObject,
The design of an ensemble of classifiers involves the definition of an aggregation mechanism that produces a single response obtained from the information provided by the classifiers. A specific aggregation methodology ...
Unsupervised fuzzy measure learning for classifier ensembles from coalitions performance
(IEEE, 2020)
info:eu-repo/semantics/article,
In Machine Learning an ensemble refers to the combination of several classifiers with the objective of improving the performance of every one of its counterparts. To design an ensemble two main aspects must be considered: ...