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Table 3 Performance comparison on real datasets

From: A machine learning framework for genotyping the structural variations with copy number variant

  Coverage InsertSize The proposed method G-Features+M-SVM G-Features+NB M-SVM NB
1 472 × 216 0.8875 0.9851 0.9777 0.9880 0.9777
2 396 × 213 0.8917 0.9098 0.9031 0.9469 0.9317
3 394 × 223 0.9083 0.6265 0.5513 0.7493 0.6766
4 291 × 175 0.9082 0.6403 0.6839 0.7089 0.8582
5 448 × 214 0.9000 0.6299 0.7254 0.7444 0.6886
6 460 × 210 0.9167 0.5881 0.3415 0.6502 0.3589
7 402 × 202 0.7258 0.4603 0.4632 0.6361 0.6217
8 422 × 207 0.8917 0.7444 0.7439 0.7660 0.7469
9 369 × 215 0.9083 0.9234 0.7667 0.9336 0.9669
Average 0.8820 0.7230 0.6840 0.7914 0.7585
Range 0.1909 0.5248 0.6362 0.3519 0.6188
  1. In practice, we adopted the multi-classification support vector machine (M-SVM) as a plus version because the Gindel is a binary classification method based on support vector machine (SVM), it will treat the other three genotypes as classification errors and lead to low accuracy when applied directly