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

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

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Official URL: http://doi.org/10.22438/jeb/40/3(SI)/Sp-21

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Bio-inspired kernels; Bio-Twin Support Vector Machine; Enzymes; Fisher; Kernel optimization
Subjects:	Q Science > QH Natural history T Technology > T Technology (General)
Divisions:	Faculty of Computer Science and Information Technology > Department of Web Technology
Depositing User:	Miss Afiqah Faiqah Mohd Hafiz
Date Deposited:	07 Dec 2021 09:18
Last Modified:	07 Dec 2021 09:18
URI:	http://eprints.uthm.edu.my/id/eprint/4626

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