Table 4 Classification results for motif-oriented data using different kernels
From: Automatic detection of exonic splicing enhancers (ESEs) using SVMs
accuracy | specificity | sensitivity | correlation | |
|---|---|---|---|---|
SVM, combined oligo kernel | 90.74% | 96.04% | 82.09% | 78.93% |
25% quantile | 90.45% | 95.4% | 81.16% | 78.42% |
median | 90.82% | 96.04% | 82.09% | 79.23% |
75% quantile | 91.22% | 96.62% | 83.25% | 79.93% |
SVM, locality improved kernel | 70.00% | 92.45% | 33.36% | 32.43% |
25% quantile | 69.16% | 89.06% | 24.45% | 30.73% |
median | 69.88% | 91.43% | 38.56% | 32.33% |
75% quantile | 70.93% | 96.49% | 41.37% | 34.49% |
Markov chain model | 68.42% | 79.26% | 50.71% | 31.44% |
25% quantile | 67.98% | 76.17% | 50.95% | 30.66% |
median | 68.29% | 77.61% | 53.7% | 31.67% |
75% quantile | 68.89% | 80.57% | 55.02% | 32.64% |