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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 ...
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: ...
Motor-imagery-based brain-computer interface using signal derivation and aggregation functions
(IEEE, 2021)
info:eu-repo/semantics/article,
Brain Computer Interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to
BCI is Motor Imagery (MI). In BCI applications, the ...
Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
(IEEE, 2021)
info:eu-repo/semantics/article,
In this work we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two ...
Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface
(Elsevier, 2024)
Artículo / Artikulua,
In this paper we propose a new version of penalty-based aggregation functions, the Multi Cost Aggregation choosing functions (MCAs), in which the function to minimize is constructed using a convex combination of two relaxed ...