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Density-Based Penalty Parameter Optimization on C-SVM

The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change...

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Detalles Bibliográficos
Autores principales: Liu, Yun, Lian, Jie, Bartolacci, Michael R., Zeng, Qing-An
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4119659/
https://www.ncbi.nlm.nih.gov/pubmed/25114978
http://dx.doi.org/10.1155/2014/851814
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author Liu, Yun
Lian, Jie
Bartolacci, Michael R.
Zeng, Qing-An
author_facet Liu, Yun
Lian, Jie
Bartolacci, Michael R.
Zeng, Qing-An
author_sort Liu, Yun
collection PubMed
description The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system's outliers. Traditional C-SVM holds a uniform parameter C for both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall.
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spelling pubmed-41196592014-08-11 Density-Based Penalty Parameter Optimization on C-SVM Liu, Yun Lian, Jie Bartolacci, Michael R. Zeng, Qing-An ScientificWorldJournal Research Article The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system's outliers. Traditional C-SVM holds a uniform parameter C for both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall. Hindawi Publishing Corporation 2014 2014-07-07 /pmc/articles/PMC4119659/ /pubmed/25114978 http://dx.doi.org/10.1155/2014/851814 Text en Copyright © 2014 Yun Liu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Yun
Lian, Jie
Bartolacci, Michael R.
Zeng, Qing-An
Density-Based Penalty Parameter Optimization on C-SVM
title Density-Based Penalty Parameter Optimization on C-SVM
title_full Density-Based Penalty Parameter Optimization on C-SVM
title_fullStr Density-Based Penalty Parameter Optimization on C-SVM
title_full_unstemmed Density-Based Penalty Parameter Optimization on C-SVM
title_short Density-Based Penalty Parameter Optimization on C-SVM
title_sort density-based penalty parameter optimization on c-svm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4119659/
https://www.ncbi.nlm.nih.gov/pubmed/25114978
http://dx.doi.org/10.1155/2014/851814
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