3.3 Results 
Outputs of the GA-SVM model include optimized kernel 
parameters and feature subset, classification result, evaluation 
of the classification accuracy, as shown in Table 1. The 
classification result is shown in Figure 4. The evolution of 
fitness value with number of generation is shown in Figure 5. 
It can be seen in Table 1 that the number of features, i.e. bands, 
selected for the use of classification was only 13, much smaller 
than the total number of bands (198). Classification accuracy 
was 92.51% when using the optimized kernel parameters and 
feature subset, comparing to 88.81% when no feature selection 
was performed. The optimized values of the two SVM kernel 
parameters were 95.0297 and 0.2021, respectively, different 
from the default values of 100 and 0.005 in ENVI, which again 
proved the necessity of optimizing the kernel parameters. 
As can be seen in Fig. 5, the fitness value increases with 
number of generation. There are sharp increase at the 3rd and 
30th generation, although the fitness value may remain 
unchanged during several generations. The acceptable fitness 
level was reached at the 30th generation. 
Although the maximum number of generation evolved was only 
40 in our study due to limited computer memory. The GA-SVM 
method has proved to be able to return acceptable result under 
the limited memory resource. It may be a safe assumption that 
provided with larger computer memory, better results may be 
gained. 
73. 20 
In conclusion, the proposed wrapper feature selection method 
GA-SVM can optimize feature subsets and SVM kernel 
parameters at the same time, therefore can be applied in feature 
selection of the hyper spectral data. 
Bengio Y., 2000. Gradient-based Optimization of Hyper 
parameters. Neural Computation, 12(8), pp. 1889-1900. 
Bi J., Bennett K.P., Embrechts M., Breneman C.M., Song M., 
2003. Dimensionality reduction via sparse support vector 
machines. J. Mach. Learn. Res. 3, pp. 1229-1243. 
Bradley P. S., Mangasarian O. L., Street W. N., 1998. Feature 
selection via mathematical programming. INFORMS Journal 
on Computing, 10, pp. 209-217. 
Chen M., Yi Y. H., Liu Zh. G., Li D. R., QIN Q. Q., 2006. 
Study of Band Selection Methods of Hyperspectral Image 
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 
Number of Generation 
Figure 5. Change of fitness with population evolvement