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

• Main Authors: Guramand, S.K., Saedudin, R.D.R., Hassan, R., Kasim, S., Ramlan, R., Salim, B. W.
• Format: Article
• Language: English
• Published: Triveni Enterprises, Lucknow (India) 2019
• Subjects: QH Natural history; T Technology (General)
• Online Access: 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

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–...