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: | 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: | |
Online Access: | http://eprints.uthm.edu.my/4626/1/AJ%202019%20%28299%29.pdf |
Similar Items
-
Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification
by: Guramand, S. K., et al.
Published: (2019) -
Kernel based online learning for imbalance multiclass classification
by: Ding, Shuya, et al.
Published: (2020) -
Evaluate the performance of SVM kernel functions for multiclass cancer classification
by: Mohd Hatta, Noramalina, et al.
Published: (2020) -
OBKA-FS: an oppositional-based binary kidney-inspired search algorithm for feature selection
by: Taqi, M. K., et al.
Published: (2017) -
Combining sampling and ensemble classifier for multiclass imbalance data learning
by: Sainin, Mohd Shamrie, et al.
Published: (2018)