Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures

In this paper we present the parallelization of the leave-one-out test: a reproducible test that is, in general, computationally expensive. Parallelization was implemented on multi-core multi-threaded architectures, using the Flynn Single Instruction Multiple Data taxonomy. This technique was used f...

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Autores:
Uribe-Hurtado, Ana Lorena
Villegas-Jaramillo, Eduardo-Jose
Orozco-Alzate, Mauricio
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/13192
Acceso en línea:
http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/4867
http://hdl.handle.net/10784/13192
Palabra clave:
Multi-core computing
Classification algorithms
Leave-one-out test
Computación con múltiples núcleos
Algoritmos de clasificación
Prueba leave-one-ou
Rights
License
Attribution 4.0 International (CC BY 4.0)
Description
Summary:In this paper we present the parallelization of the leave-one-out test: a reproducible test that is, in general, computationally expensive. Parallelization was implemented on multi-core multi-threaded architectures, using the Flynn Single Instruction Multiple Data taxonomy. This technique was used for the preprocessing and processing stages of two classification algorithms that are oriented to enrich the representation in small sample cases: the nearest feature line (NFL) algorithm and the rectified nearest feature line segment (RNFLS) algorithm. Results show an acceleration of up to 18.17 times with the smallest dataset and 29.91 times with the largest one, using the most costly algorithm (RNFLS) whose complexity is O(n4). The paper also shows the pseudo-codes of the serial and parallel algorithms using, in the latter case, a notation that describes the way the parallelization was carried out as a function of the threads.