Table 2.

The average AUROC for each of the methods on banana data

	LR	OC-SVM	OC-SVM (Neg)	TC-SVM	TC-SVM (CS)	OP-SVM	OP-SVM (CS)
5 (0.18%)	0.513 (±0.004)	0.857 (±0.005)	0.863 (±0.006)	0.867 (±0.007)	0.912 (±0.003)	0.929 (±0.002)	0.925 (±0.003)
10 (0.37%)	0.520 (±0.004)	0.865 (±0.004)	0.878 (±0.003)	0.877 (±0.004)	0.912 (±0.002)	0.932 (±0.002)	0.926 (±0.003)
15 (0.55%)	0.517 (±0.004)	0.869 (±0.004)	0.883 (±0.003)	0.898 (±0.003)	0.914 (±0.002)	0.935 (±0.001)	0.932 (±0.002)
20 (0.74%)	0.517 (±0.004)	0.872 (±0.004)	0.885 (±0.002)	0.904 (±0.003)	0.917 (±0.002)	0.935 (±0.001)	0.933 (±0.001)
25 (0.92%)	0.521 (±0.004)	0.877 (±0.003)	0.884 (±0.002)	0.909 (±0.003)	0.923 (±0.002)	0.935 (±0.001)	0.933 (±0.002)
30 (1.11%)	0.525 (±0.004)	0.875 (±0.003)	0.887 (±0.002)	0.919 (±0.003)	0.925 (±0.002)	0.936 (±0.001)	0.934 (±0.002)
35 (1.29%)	0.523 (±0.005)	0.875 (±0.003)	0.886 (±0.002)	0.927 (±0.002)	0.931 (±0.002)	0.936 (±0.001)	0.935 (±0.001)
40 (1.48%)	0.527 (±0.004)	0.876 (±0.003)	0.889 (±0.002)	0.930 (±0.002)	0.940 (±0.002)	0.937 (±0.001)	0.937 (±0.001)
45 (1.66%)	0.530 (±0.004)	0.876 (±0.003)	0.890 (±0.002)	0.937 (±0.002)	0.945 (±0.002)	0.938 (±0.001)	0.939 (±0.001)
50 (1.85%)	0.528 (±0.004)	0.876 (±0.003)	0.887 (±0.002)	0.938 (±0.002)	0.944 (±0.002)	0.937 (±0.001)	0.938 (±0.002)

The proportion of positive examples in training sets was varied from 0.18% to 1.85%. The best AUROC values are shown in boldface, with the second best shown in italics.

AUROC, area under the receiver operating characteristic curve; LR, logistic regression; OC-SVM, one-class support vector machine classification; OP-SVM, one-plus-class support vector machine; TC-SVM, two-class support vector machine classification.