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Reseach Article

On Support Vector Machine Ensembles for Classification of Recombination Breakpoint Regions in Saccharomyces Cerevisiae

by Ashok Kumar Dwivedi, Usha Chouhan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 13
Year of Publication: 2014
Authors: Ashok Kumar Dwivedi, Usha Chouhan
10.5120/18975-0475

Ashok Kumar Dwivedi, Usha Chouhan . On Support Vector Machine Ensembles for Classification of Recombination Breakpoint Regions in Saccharomyces Cerevisiae. International Journal of Computer Applications. 108, 13 ( December 2014), 44-48. DOI=10.5120/18975-0475

@article{ 10.5120/18975-0475,
author = { Ashok Kumar Dwivedi, Usha Chouhan },
title = { On Support Vector Machine Ensembles for Classification of Recombination Breakpoint Regions in Saccharomyces Cerevisiae },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 13 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 44-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number13/18975-0475/ },
doi = { 10.5120/18975-0475 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:42:55.959749+05:30
%A Ashok Kumar Dwivedi
%A Usha Chouhan
%T On Support Vector Machine Ensembles for Classification of Recombination Breakpoint Regions in Saccharomyces Cerevisiae
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 13
%P 44-48
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recombination has major influence on evolution. Recombination occurs at specific region on chromosomes more frequently than other regions. Chromosomal region where recombination occurs more frequently is hot recombination region, whereas, the region where recombination occurs less frequently is cold recombination region. In this paper, supervised machine learning model based on support vector machine and ensembles of support vector machine have been devised for the efficient and effective classification of hot and cold recombination regions based on the compositional features of nucleotide sequences. Models were validated using tenfold cross validation techniques. These models gave high classification accuracy of 87. 0%, 91. 58%, and 92. 14 % using support vector machine and its boosting and bagging ensembles respectively. Moreover, support vector machine ensemble with bagging gave remarkably high area under receiver operating curve of . 9580. Furthermore, results indicate that bagging ensembles achieved the best result while used for the performance improvement of support vector machines.

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Index Terms

Computer Science
Information Sciences

Keywords

Recombination Support Vector Machine Boosting Bagging Classification