封面图片

Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification

The function of enzymes is performed differently depending on their bio-chemical mechanisms and important to the prediction of protein structure and function. In order to overcome the weaknesses of imbalance data distribution in subclasses prediction we proposed Bio-Twin Support Vector Machine (Bio–...

全面介绍

Saved in:
书目详细资料
Main Authors: Guramand, S.K., Saedudin, R.D.R., Hassan, R., Kasim, S., Ramlan, R., Salim, B. W.
格式: Article
语言:English
出版: Triveni Enterprises, Lucknow (India) 2019
主题:
QH Natural history
T Technology (General)
在线阅读:http://eprints.uthm.edu.my/4626/1/AJ%202019%20%28299%29.pdf
http://eprints.uthm.edu.my/4626/
http://doi.org/10.22438/jeb/40/3(SI)/Sp-21
标签: 添加标签
没有标签, 成为第一个标记此记录!
id my.uthm.eprints.4626
record_format eprints
spelling my.uthm.eprints.46262021-12-07T09:18:25Z http://eprints.uthm.edu.my/4626/ Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification Guramand, S.K. Saedudin, R.D.R. Hassan, R. Kasim, S. Ramlan, R. Salim, B. W. QH Natural history T Technology (General) The function of enzymes is performed differently depending on their bio-chemical mechanisms and important to the prediction of protein structure and function. In order to overcome the weaknesses of imbalance data distribution in subclasses prediction we proposed Bio-Twin Support Vector Machine (Bio–TWSVM). The TWSVM approach as also allow for kernel optimization where in this study we have introduced the bio-inspired kernels such as the Fisher, spectrum and mismatch kernels which at the same time incorporate the biological information regarding the protein evolution in the classification process. Triveni Enterprises, Lucknow (India) 2019 Article PeerReviewed text en http://eprints.uthm.edu.my/4626/1/AJ%202019%20%28299%29.pdf Guramand, S.K. and Saedudin, R.D.R. and Hassan, R. and Kasim, S. and Ramlan, R. and Salim, B. W. (2019) Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification. Journal of Environmental Biology, 40. pp. 563-576. ISSN 0254-870 http://doi.org/10.22438/jeb/40/3(SI)/Sp-21
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic QH Natural history
T Technology (General)
spellingShingle QH Natural history
T Technology (General)
Guramand, S.K.
Saedudin, R.D.R.
Hassan, R.
Kasim, S.
Ramlan, R.
Salim, B. W.
Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification
description The function of enzymes is performed differently depending on their bio-chemical mechanisms and important to the prediction of protein structure and function. In order to overcome the weaknesses of imbalance data distribution in subclasses prediction we proposed Bio-Twin Support Vector Machine (Bio–TWSVM). The TWSVM approach as also allow for kernel optimization where in this study we have introduced the bio-inspired kernels such as the Fisher, spectrum and mismatch kernels which at the same time incorporate the biological information regarding the protein evolution in the classification process.
format Article
author Guramand, S.K.
Saedudin, R.D.R.
Hassan, R.
Kasim, S.
Ramlan, R.
Salim, B. W.
author_facet Guramand, S.K.
Saedudin, R.D.R.
Hassan, R.
Kasim, S.
Ramlan, R.
Salim, B. W.
author_sort Guramand, S.K.
title Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification
title_short Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification
title_full Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification
title_fullStr Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification
title_full_unstemmed Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification
title_sort optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification
publisher Triveni Enterprises, Lucknow (India)
publishDate 2019
url http://eprints.uthm.edu.my/4626/1/AJ%202019%20%28299%29.pdf
http://eprints.uthm.edu.my/4626/
http://doi.org/10.22438/jeb/40/3(SI)/Sp-21
_version_ 1738581276623896576
score 13.251813

相似书籍