Cybercrime: Identification and Prediction Using Machine Learning Techniques

Table 1

Training data for the SVM classifier.

Sl. no.	Training dataset (average)	SVM Struct. Support Vectors	SVM Struct. Scale Data Shift	SVM Struct. Scale Data Scale Factor	SVM Struct. Alpha	SVM Struct. Bias	SVM Struct. Support Vectorization	Mean-squared error for regression using 10-fold cross-validation:	Misclassification rate using stratified 10-fold cross-validation:	Confusion matrix using stratified 10-fold cross-validation:
								cvMSE	cvMCR	cfMat
1	7.850	0.314	−7.102	0.260	0.625	1.958	6.000	0.003	0.150	0 1 0; 12 69 2; 0 0 16
2	8.040	0.403	−7.102	0.260	0.625	1.958	10.000	0.003	0.150
3	8.000	0.379	−7.102	0.260	0.625	1.958	11.000	0.003	0.150
4	7.750	0.317	−7.102	0.260	0.625	1.958	13.000	0.003	0.150
5	7.730	0.462	−7.102	0.260	0.625	1.958	14.000	0.003	0.150
6	8.310	0.384	−7.102	0.260	0.625	1.958	15.000	0.003	0.150
7	7.780	0.743	−7.102	0.260	0.625	1.958	49.000	0.003	0.150
8	7.670	0.936	−7.102	0.260	−1.250	1.958	50.000	0.003	0.150
9	8.220	0.749	−7.102	0.260	0.625	1.958	51.000	0.003	0.150
10	8.650	0.837	−7.102	0.260	−2.500	1.958	52.000	0.003	0.150
11	8.560	0.533	−7.102	0.260	0.625	1.958	53.000	0.003	0.150
12	8.170	0.689	−7.102	0.260	0.625	1.958	54.000	0.003	0.150
13	8.320	0.754	−7.102	0.260	0.625	1.958	55.000	0.003	0.150
14	8.880	0.790	−7.102	0.260	−2.500	1.958	56.000	0.003	0.150
15	8.580	0.319	−7.102	0.260	0.625	1.958	62.000	0.003	0.150
16	8.180	0.907	−7.102	0.260	−2.500	1.958	97.000	0.003	0.150
17	7.320	0.561	−7.102	0.260	0.625	1.958	98.000	0.003	0.150
18	7.230	0.343	−7.102	0.260	0.625	1.958	100.00	0.003	0.150