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Table 4 Classification accuracy of the support vector machine with three different kernels with default parameters

From: PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel

Dataset

Kernel

Sensitivity

Specificity

Accuracy

Precision

Q1

Linear

59.24 \(\pm\) 0.90

63.02 \(\pm\) 3.42

61.13 \(\pm\) 1.66

61.63 \(\pm\) 2.16

 

Laplace

58.86 \(\pm\) 1.71

63.21 \(\pm\) 2.42

61.04 \(\pm\) 1.86

61.56 \(\pm\) 2.07

 

Radial

68.64 \(\pm\) 1.55

51.86 \(\pm\) 3.42

60.25 \(\pm\) 1.54

58.81 \(\pm\) 1.55

Q2

Linear

63.32 \(\pm\) 2.06

66.21 \(\pm\) 1.39

64.76 \(\pm\) 1.48

65.19 \(\pm\) 1.43

 

Laplace

64.07 \(\pm\) 1.83

64.32 \(\pm\) 2.11

64.20 \(\pm\) 1.49

64.25 \(\pm\) 1.57

 

Radial

67.52 \(\pm\) 3.67

60.62 \(\pm\) 1.30

64.07 \(\pm\) 1.93

63.14 \(\pm\) 1.47

Q3

Linear

66.10 \(\pm\) 4.34

63.89 \(\pm\) 2.85

65.01 \(\pm\) 2.20

64.67 \(\pm\) 1.94

 

Laplace

70.56 \(\pm\) 4.25

60.81 \(\pm\) 2.61

65.69 \(\pm\) 1.93

64.29 \(\pm\) 1.56

 

Radial

67.61 \(\pm\) 3.52

60.75 \(\pm\) 4.74

64.18 \(\pm\) 1.11

63.36 \(\pm\) 1.78

Q4

Linear

59.26 \(\pm\) 2.29

57.94 \(\pm\) 3.71

58.61 \(\pm\) 2.50

58.53 \(\pm\) 2.63

 

Laplace

64.91 \(\pm\) 2.68

53.11 \(\pm\) 2.27

59.01 \(\pm\) 1.71

58.06 \(\pm\) 1.48

 

Radial

59.70 \(\pm\) 2.52

60.29 \(\pm\) 1.98

60.01 \(\pm\) 1.51

60.05 \(\pm\) 1.45

  1. Classification was made with each sub dataset and performance metrics were computed following repeated cross validation where the experiment was repeated 100 times. In terms of accuracy, performances are higher for the Laplace kernel for Q2 and Q3 sub-datasets, whereas linear and RBF kernel performed better in Q1 and Q4 respectively. Performance metrics are higher for Q2 and Q3 sub-datasets than that of Q1 and Q4. The accuracies are seen to be more stable for RBF kernel, barring few exceptions