Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

Table 5

Result of Experiment  1.

Dataset	Number of original features	Group 1		Group 2		Group 3
		No feature selection		Feature selection by AFSA		Feature selection by MAFSA
		Number of selected features	Average accuracy rate (%)	Number of selected features	Average accuracy rate (%)	Number of selected features	Average accuracy rate (%)
Bupa	6	6	58.85	3.3	59.73^**	3.2^*	59.73^**
Pima	8	8	77.21	5^*	80.47^**	5.6	80.47^**
Glass	9	9	48.11	4.8	64.89^**	4.4^*	64.89^**
Vowel	10	10	71.91	9	77.57	8.5^*	77.67^**
Heart	13	13	81.85	7.2	94.07^**	7.1^*	94.07^**
Australian	14	14	85.79	8	88.26^**	7.7^*	88.26^**
Vehicle	18	18	71.98	12.4^*	75.41	12.4^*	75.53^**
Robot	24	24	85.90	17^*	86.73	17.6	87.48^**
German	24	24	74.90	14.7	82.90	12.4^*	83.50^**
Sonar	60	60	73.19	29.7	93.33	27.2^*	94.28^**

It indicates the fewest number of selected features.
It indicates the highest classification accuracy.