Table 2.
Average accuracy provided by the methods using different databases.
| Dataset | Method using DE | Method using PSO | Method using CS | |||
|---|---|---|---|---|---|---|
| Tr. cr. | Te. cr. | Tr. cr. | Te. cr. | Tr. cr. | Te. cr. | |
| Wine | 0.9796 | 0.8744 | 0.9782 | 0.8879 | 0.9831 | 0.9078 |
| Iris plant | 0.9933 | 0.9833 | 0.9933 | 0.97 | 0.9942 | 0.9467 |
| Glass | 0.8158 | 0.7411 | 0.8178 | 0.7457 | 0.8080 | 0.7646 |
| Diabetes | 0.8038 | 0.7371 | 0.7990 | 0.7619 | 0.8051 | 0.7477 |
| Liver | 0.7620 | 0.6870 | 0.7591 | 0.6754 | 0.7609 | 0.6536 |
| Object recognition | 1 | 0.9850 | 1 | 0.9950 | 1 | 1 |
Tr. cr = training classification rate, Te. cr. = testing classification rate.