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–...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Triveni Enterprises, Lucknow (India)
2019
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4626/ http://eprints.uthm.edu.my/4626/1/AJ%202019%20%28299%29.pdf |
| Summary: | 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. |
|---|