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Table 3 Averaged AUC values for comparison of different methods

From: Hadamard Kernel SVM with applications for breast cancer outcome predictions

Methods

     

Datasets

Linear kernel

Quadratic kernel

RBF kernel

Hadamard kernel

Correlation kernel

GSE1872

0.3788 ± 0.1019

0.3686 ± 0.1136

0.2117 ± 0.0584

1.000 ± 0.000

0.9989 ± 0.0018

GSE32394

0.9456 ± 0.0312

0.5544 ± 0.1248

0.9344 ± 0.0254

0.9589 ± 0.0166

0.9233 ± 0.0294

GSE59246

0.8977 ± 0.0172

0.5386 ± 0.0579

0.8431 ± 0.0379

0.9022 ± 0.0145

0.8562 ± 0.0113

GSE59993

0.8283 ± 0.0226

0.5935 ± 0.0694

0.8347 ± 0.0182

0.8855 ± 0.0088

0.7869 ± 0.0144

GSE25055

0.8575 ± 0.0182

0.4743 ± 0.0393

0.8196 ± 0.0203

0.8653 ± 0.0171

0.7654 ± 0.0152

GSE1379

0.6205 ± 0.0481

0.5237 ± 0.0701

0.6743 ± 0.0427

0.7300 ± 0.0375

0.6419 ± 0.0453

  1. The bold face represents the best performance detected for different compared methods
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BMC Systems Biology

ISSN: 1752-0509

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