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

Table 6

Result of Experiment  2.

Dataset	Number of original features	Feature selection; by AFSA		Feature selection; by MAFSA
		Number of; selected; features	Average accuracy rate (%)	Number of; selected; features	Average accuracy rate (%)
Bupa	6	4.1	84.94	3.9^*	85.22^**
Pima	8	4.8	83.72	4.2^*	83.85^**
Glass	9	4.9^*	89.69	5.1	91.58^**
Vowel	10	7.8	100^**	7^*	100^**
Heart	13	6.1^*	97.03	6.2	97.77^**
Australian	14	7.1	93.62^**	5.7^*	93.33
Vehicle	18	11.2	91.01	11^*	92.08^**
Robot	24	7.6^*	96.57	8.3	97.25^**
German	24	14.2	83.7	13.7^*	84.6^**
Sonar	60	27.2^*	99.04	29.1	100^**

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