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Table 7 Comparison of classification performance of all classifiers on all datasets. Comparison of classification performance (AUC) among selected machine learning methods namely, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), C4.5, Naïve Bayes (NB), Penalized Logistic Regression (PLR), as well as RL (baseline), TRL, and TRL-FM on all datasets. Note that for TRL, the AUC for the highest performing source is shown, while for TRL-FM, the medium of knowledge transfer is the union of all FMs. In addition, the average (AVG) AUC performances, including average standard error of the mean, for each classifier across the entire datasets are provided (see Additional File 5 for detailed results)

From: Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data

Dataset SVM LDA RF C4.5 NB PLR RL TRL TRL-FM
Emblom 1.00 1.00 1.00 0.98 0.96 0.98 0.97 0.97 0.94
Freije 0.74 0.72 0.72 0.73 0.82 0.76 0.76 0.78 0.80
Gravendeel 0.52 0.59 0.59 0.53 0.63 0.56 0.49 0.49 0.59
KangA 0.93 0.86 0.86 0.79 0.94 0.90 0.86 0.93 0.97
KangB 0.91 0.87 0.87 0.87 0.91 0.95 0.83 0.91 0.95
Konishi 0.90 0.68 0.68 0.74 0.90 0.90 0.78 0.83 0.95
Lapointe 0.96 0.91 0.91 0.94 0.97 0.96 0.93 0.93 0.97
Larsson 0.33 0.67 0.67 0.58 0.67 0.67 0.75 0.75 1.00
Nanni 0.70 0.61 0.61 0.44 0.57 0.65 0.54 0.54 0.64
Pardo 0.83 0.85 0.85 0.63 0.80 0.88 0.85 0.90 0.95
Paugh 0.48 0.45 0.45 0.43 0.50 0.45 0.51 0.52 0.54
Petalidis 0.75 0.71 0.71 0.69 0.80 0.80 0.83 0.88 0.91
Phillips 0.73 0.70 0.70 0.66 0.75 0.80 0.66 0.73 0.78
Singh 0.89 0.90 0.90 0.89 0.88 0.91 0.89 0.89 0.93
Varambally 1.00 0.92 0.92 0.67 1.00 1.00 0.83 1.00 1.00
Wallace 0.82 0.85 0.85 0.76 0.81 0.87 0.76 0.81 0.84
Welsh 0.94 0.66 0.66 0.79 0.93 0.94 0.92 0.95 0.93
Yamanaka 0.57 0.57 0.57 0.56 0.71 0.56 0.50 0.50 0.79
Yang 0.69 0.51 0.51 0.89 0.57 0.73 0.94 0.94 0.89
Yu 0.94 0.93 0.93 0.80 0.97 0.94 0.88 0.90 0.93
AVG AUC 0.77 0.74 0.74 0.71 0.80 0.81 0.77 0.80 0.86
AVG SEM 0.06 0.07 0.07 0.07 0.06 0.05 0.07 0.06 0.04