Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

Figures & data

Figure 1. Mapping characteristics based on four kinds of kernel functions (a) RBF kernel function, (b) polynomial kernel function, (c) linear kernel function, and (d) sigmoid kernel function.

Figure 2. Mapping characteristics based on three kinds of mixed kernel functions (a) RBF kernel mixes with linear function, (b) RBF kernel mixes with polynomial kernel function, and (c) RBF kernel mixes with sigmoid kernel function.

Figure 3. The flowchart of the improved PSO-mixed kernel SVM algorithm.

Table 1. Main characteristics of datasets.

	Instances	Features	Classes
Parkinsons	195	22	2
Statlog (Heart)	270	13	2
DLBCL-B	180	661	3
Diabetes	768	8	2
Lung-A	197	1000	4

Table 2. The optimization parameters c, g, u of PSO-RBF kernel, PSO-mixed kernel and improved PSO-mixed kernel SVM and classification accuracy rate of the test set.

	c, g, u			Accuracy rate of test set (%)
Datasets	PSO-RBF kernel	PSO-mixed kernel	Improved PSO-mixed kernel	PSO-RBF kernel	PSO-mixed kernel	Improved PSO-mixed kernel
Parkinsons	1.53, 3.98, –	1.46, 4.78, 0.76	1.45, 4.65, 0.80	97.75	98.88	98.88
Heart	7.98, 1.07, –	4.15, 4.21, 0.44	4.15, 4.21, 0.44	90.57	95.6	95.6
DLBCL-B	4.53, 0.36, –	1.88, 0.65, 0.76	0.49, 26.48, 0.81	81.45	84	85.82
Diabetes	1.57, 0.30, –	58.07, 21.73, 0.03	12.53, 83.56, 0.10	84.83	86.21	87.93
Lung-A	4.12, 0.001, –	0.98, 0.08, 0.97	0.01, 72.63, 0.92	92.07	93.09	93.42

Figure 4. The line chart of (a) classification accuracy rate; (b) computational sacrifice of optimized PSO-RBF kernel, PSO-mix kernel, and improved PSO-mixed kernel support vector machine.