Table 7 Comparative performance of various optimization techniques for SVM classification of wheat genotypes.
From: Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes
Classifier | Optimum parameter values | Accuracy | Kappa | ||
|---|---|---|---|---|---|
Training | Testing | Training | Testing | ||
SVM | C = 1 | 0.938 | 0.905 | 0.932 | 0.897 |
σ = 1 | |||||
GS-SVM | C = 50 | 0.930 | 0.892 | 0.847 | 0.766 |
σ = 0.01 | |||||
RS-SVM | C = 96.51 | 0.934 | 0.899 | 0.881 | 0.818 |
σ = 0.009 | |||||
GA-SVM | C = 472.48 | 0.938 | 0.905 | 0.915 | 0.871 |
σ = 0.12 | |||||
DE-SVM | C = 476.95 | 0.938 | 0.905 | 0.915 | 0.870 |
σ = 0.04 | |||||
PSO-SVM | C = 863.69 | 0.942 | 0.911 | 0.949 | 0.922 |
σ = 0.01 | |||||
