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. 2013 Feb 23;14:64. doi: 10.1186/1471-2105-14-64

Table 4.

Performance of the classifiers on real gene expression data sets for the two class classification tasks

Data set Method λ∗a # genes PA PA1 PA2 g-means AUC
Ivshina
 PAM
 0
 22283
 0.84
 0.79
 0.85
 0.82
 0.85
(ER)
 GM-PAM
 4.83
 51
 0.82
 0.91
 0.81
 0.86
 0.90
 
 ALP
 0
 22283
 0.84
 0.82
 0.84
 0.83
 0.86
 
 GM-ALP
 58.24
 26
 0.85
 0.91
 0.84
 0.87
 0.88
 
 AHP
 185.56
 20
 0.89
 0.88
 0.89
 0.88
 0.90
 
 GM-AHP
 69.58
 115
 0.89
 0.88
 0.89
 0.89
 0.91
Wang
 PAM
 3.71
 14
 0.61
 0.60
 0.62
 0.61
 0.62
 
 GM-PAM
 3.71
 14
 0.60
 0.60
 0.62
 0.61
 0.63
 
 ALP
 8.26
 654
 0.56
 0.57
 0.55
 0.56
 0.63
 
 GM-ALP
 8.26
 654
 0.56
 0.56
 0.56
 0.56
 0.63
 
 AHP
 21.95
 135
 0.56
 0.56
 0.56
 0.56
 0.65
 
 GM-AHP
 21.95
 135
 0.56
 0.56
 0.55
 0.56
 0.63
Korkola
 PAM
 0.19
 7073
 0.65
 0.71
 0.57
 0.64
 0.64
 
 GM-PAM
 0.19
 7073
 0.65
 0.71
 0.57
 0.64
 0.64
 
 ALP
 4.87
 155
 0.64
 0.65
 0.62
 0.63
 0.64
 
 GM-ALP
 4.87
 155
 0.69
 0.74
 0.62
 0.67
 0.69
 
 AHP
 0.76
 1308
 0.58
 0.68
 0.43
 0.54
 0.58
  GM-AHP 0.76 1308 0.62 0.71 0.48 0.58 0.60

The table reports the same information as Table 1; # genes is the number of active genes. Optimal thresholds were estimated with 5-fold CV and the accuracy measures with LOOCV; see text for details.

aFor AHP and GM-AHP only λθ was optimized while λγ was set to zero.