VCI-LSTM: Vector choquet integral-based long short-term memory
Fecha
2022Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
AEI//PID2019-108392GBI00
Impacto
|
10.1109/TFUZZ.2022.3222035
Resumen
Choquet integral is a widely used aggregation operator on one-dimensional and interval-valued information, since it is able to take into account the possible interaction among data. However, there are many cases where the information taken into account is vectorial, such as Long Short-Term Memories (LSTM). LSTM units are a kind of Recurrent Neural Networks that have become one of the most powerfu ...
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Choquet integral is a widely used aggregation operator on one-dimensional and interval-valued information, since it is able to take into account the possible interaction among data. However, there are many cases where the information taken into account is vectorial, such as Long Short-Term Memories (LSTM). LSTM units are a kind of Recurrent Neural Networks that have become one of the most powerful tools to deal with sequential information since they have the power of controlling the information flow. In this paper, we first generalize the standard Choquet integral to admit an input composed by <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-dimensional vectors, which produces an <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-dimensional vector output. We study several properties and construction methods of vector Choquet integrals. Then, we use this integral in the place of the summation operator, introducing in this way the new VCI-LSTM architecture. Finally, we use the proposed VCI-LSTM to deal with two problems: sequential image classification and text classification. [--]
Materias
Aggregation functions,
Choquet integral,
LSTM,
Recurrent neural networks,
Vector choquet integral
Editor
IEEE
Publicado en
IEEE Transactions on Fuzzy Systems, 2022
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila
Versión del editor
Entidades Financiadoras
Grant PID2019-108392GBI00 funded by MCIN/AEI/10.13039/501100011033, by CNPq (Proc. 301618/2019-4), FAPERGS (Proc. 19/2551-0001660), by Tracasa Instrumental and the Immigration Policy and Justice Department of the Government of Navarre and by the project VEGA 1/0267/21.