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

Main Article Content

Ana Lorena Uribe-Hurtado http://orcid.org/0000-0002-1424-2372
Eduardo-Jose Villegas-Jaramillo http://orcid.org/0000-0002-7563-2913
Mauricio Orozco-Alzate http://orcid.org/0000-0002-5937-6382

Keywords

Multi-core computing, classification algorithms, leave-oneout test

Abstract

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. 

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