Listar Artículos de revista ISC - ISC aldizkari artikuluak por tema "Classification"
Mostrando ítems 1-5 de 5
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Addressing the overlapping data problem in classification using the one-vs-one decomposition strategy
Sáez, José Antonio; Galar Idoate, Mikel
; Krawczyk, Bartosz (IEEE, 2019)
Artículo / Artikulua 
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 ... -
Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
Fumanal Idocin, Javier ; Takáč, Zdenko; Fernández Fernández, Francisco Javier ; Sanz Delgado, José Antonio; Goyena Baroja, Harkaitz; Lin, Chin-Teng; Wang, Yu-Kai; Bustince Sola, Humberto (IEEE, 2021)
Artículo / Artikulua
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 ... -
Motor-imagery-based brain-computer interface using signal derivation and aggregation functions
Fumanal Idocin, Javier ; Wang, Yu-Kai; Lin, Chin-Teng; Fernández Fernández, Francisco Javier ; Sanz Delgado, José Antonio ; Bustince Sola, Humberto (IEEE, 2021)
Artículo / Artikulua
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 ... -
A supervised fuzzy measure learning algorithm for combining classifiers
Uriz Martín, Mikel Xabier; Paternain Dallo, Daniel ; Bustince Sola, Humberto ; Galar Idoate, Mikel (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
Uriz Martín, Mikel Xabier ; Paternain Dallo, Daniel ; Domínguez Catena, Iris ; Bustince Sola, Humberto ; Galar Idoate, Mikel (IEEE, 2020)
Artículo / Artikulua
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: ...
