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Table 1 Prediction accuracies achieved by SVM, RVKDE, G2DE, C4.5 and RIPPER.

From: Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

 

Kernel based classifiers

Logic based classifiers

Feature set

SVM

RVKDE

G 2 DE

G 2 DE-2

C4.5

RIPPER

1

80.17%

77.59%

80.39%

80.60%

77.80%

76.72%

2

93.32%

92.46%

92.03%

93.10%

90.95%

90.52%

3

91.60%

91.16%

91.60%

92.46%

91.16%

91.38%

4

78.66%

79.53%

78.66%

80.17%

77.37%

76.72%

Average

85.94%

85.18%

85.67%

86.58%

84.32%

83.84%

#kernels

361

920

6

36

10

9

  1. The best performance among each feature set is highlighted with bold font. The G2DE-2 indicates the two-stage G2DE, which uses the first stage G2DE to cluster samples and than uses the second stage G2DE to classify each clusters. The #kernels indicate number of kernels in average, where the numbers of logic based classifiers indicate the number of rules they deliver.