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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 ...
Addressing the overlapping data problem in classification using the one-vs-one decomposition strategy
(IEEE, 2019)
info:eu-repo/semantics/article,
Learning good-performing classifiers from data with easily separable classes is not usually a difficult task for most of the algorithms. However, problems affecting classifier performance may arise when samples from different ...
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: ...