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. (Triveni Enterprises, Lucknow (India), 2019)
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.