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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 ...
A study of different families of fusion functions for combining classifiers in the one-vs-one strategy
(Springer, 2018)
info:eu-repo/semantics/conferenceObject,
In this work we study the usage of different families of fusion functions for combining classifiers in a multiple classifier system of One-vs-One (OVO) classifiers. OVO is a decomposition strategy used to deal with multi-class ...
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: ...
Orness for real m-dimensional interval-valued OWA operators and its application to determine a good partition
(Taylor & Francis, 2019)
info:eu-repo/semantics/article,
Ordered Weighted Averaging (OWA) operators are a profusely applied class of averaging aggregation functions, i.e. operators that always yield a value between the minimum and the maximum of the inputs. The orness measure ...
OWA operators based on admissible permutations
(IEEE, 2019)
info:eu-repo/semantics/conferenceObject,
In this work we propose a new OWA operator defined on bounded convex posets of a vector-lattice. In order to overcome the non-existence of a total order, which is necessary to obtain a non-decreasing arrangement of the ...