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

Guramand, S.K. and Saedudin, RD Rohmat and Hassan, R and Kasim, Shahreen and Ramlan, Rohaizan 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 0254-8704

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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: T Technology > T Technology (General)
Depositing User: Mr Abdul Rahim Mat Radzuan
Date Deposited: 02 Feb 2020 04:01
Last Modified: 02 Feb 2020 04:01
URI: http://eprints.uthm.edu.my/id/eprint/12079
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