Figure 3. A brief schema of the CoDOA-SVM approach-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
| Main Authors: | Utku Kose, Gur Emre Guraksin, Omer Deperlioglu |
|---|---|
| Format: | info Image eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2016
|
| Subjects: | |
| Online Access: |
https://www.edusoft.ro/brain/index.php/brain/issue/view/38 |
Daftar Isi:
- In the Equation 23, TP stands for true classified diabetes positive individuals; TN stands for true classified diabetes negative individuals; FP stands for false classified diabetes positive individuals and finally, FN stands for false classified diabetes negative individuals. • After determining good (optimum) particles, default CoDOA steps are run. • After achieving the total iteration number, it is allowed to train the SVM via optimum Gauss (RBF) kernel function parameters, by using the optimum particle value [sigma (σ) value]. • The trained SVM is now ready for the classification and so is diabetes determination process. A brief schema of the CoDOA-SVM approach is also provided in Figure 3.