Table 2

Feature mapping method performance in RBF-epsilon regression SVM modeling, alternatively training and testing between dataset2431 and datatset579 and 10 × cross validation within dataset2431 or datatset579

train2431

train579



test579

test2431 10 × cross validation

test2431

test579 10 × cross validation





Method

FN2431

FN579

R

MSE

R

MSE

R

MSE

R

MSE


1-

84

0.510

0.095

0.711

0.026

0.485

0.054

0.562

0.079

2-

23

0.379

0.105

0.640

0.029

0.367

0.069

0.500

0.087

3-

23

0.130

0.115

0.094

0.046

0.017

0.138

0.026

0.118

4-

24

0.202

0.115

0.293

0.041

0.214

0.073

0.214

0.041

5-

32

0.214

0.112

0.243

0.042

0.164

0.046

0.194

0.107

11-

1360

0.247

0.109

0.559

0.033

0.192

0.055

0.469

0.088

13-

43

0.045

0.111

0.277

0.042

0.071

0.104

0.262

0.105

14-

22

0.022

0.107

0.272

0.045

0.020

0.067

0.182

0.118


Method numbers are from Table 1.

FNdataset = Feature Number count from dataset

R = Pearson correlation coefficient, values with a are not able to reject the HO: R = 0.

MSE = mean squared error

Peek BMC Bioinformatics 2007 8:182   doi:10.1186/1471-2105-8-182

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