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asMODiTS: An application of surrogate models to optimize Time Series symbolic discretization through archive-based training set update strategy.
Márquez-Grajales, Aldo; Mezura-Montes, Efrén; Acosta-Mesa, Héctor-Gabriel; Salas-Martínez, Fernando.
Afiliación
  • Márquez-Grajales A; Artificial Intelligence Research Institute, University of Veracruz, Campus Sur Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, Xalapa, Veracruz 91097, Mexico.
  • Mezura-Montes E; Artificial Intelligence Research Institute, University of Veracruz, Campus Sur Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, Xalapa, Veracruz 91097, Mexico.
  • Acosta-Mesa HG; Artificial Intelligence Research Institute, University of Veracruz, Campus Sur Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, Xalapa, Veracruz 91097, Mexico.
  • Salas-Martínez F; El Colegio de Veracruz, Carrillo Puerto 26, Zona Centro, Xalapa, Veracruz 91000, Mexico.
MethodsX ; 13: 102840, 2024 Dec.
Article en En | MEDLINE | ID: mdl-39071996
ABSTRACT
The enhanced multi-objective symbolic discretization for time series (eMODiTS) uses an evolutionary process to identify the appropriate discretization scheme in the Time Series Classification (TSC) task. It discretizes using a unique alphabet cut for each word segment. However, this kind of scheme has a higher computational cost. Therefore, this study implemented surrogate models to minimize this cost. The general procedure is summarized below.•The K-nearest neighbor for regression, the support vector regression model, and the Ra- dial Basis Functions neural networks were implemented as surrogate models to estimate the objective values of eMODiTS, including the discretization process.•An archive-based update strategy was introduced to maintain diversity in the training set.•Finally, the model update process uses a hybrid (fixed and dynamic) approach for the surrogate model's evolution control.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MethodsX Año: 2024 Tipo del documento: Article País de afiliación: México Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MethodsX Año: 2024 Tipo del documento: Article País de afiliación: México Pais de publicación: Países Bajos