Table 1

Overview of individual classifier performance and definition of ensembles
Classifier Method Features Sensitivity Specificity AUC Ensemble 1 Ensemble 2 Ensemble 3 Ensemble 4 Ensemble 5
Genomics 1 LDA 24 0.73 0.90 0.73 X X X X
Genomics 2 SVM 50 0.82 0.95 0.96 X X
Genomics 3 RF 50 0.64 0.95 0.92 X X X
Genomics 4 EN 43 0.73 1.00 0.93 X X
Genomics 5 EN 174 0.73 1.00 0.95 X X
Proteomics 1 SVM 12 0.64 0.95 0.94 X X X X
Proteomics 2 EN 10 0.64 0.81 0.90 X X
Proteomics 3 SVM 33 0.55 0.81 0.83 X X
Proteomics 4 EN 13 0.55 0.86 0.85 X X
Proteomics 5 SVM 13 0.64 0.95 0.94 X X

Shown is a list of 5 genomic and 5 proteomic classifiers, their individual classification performance and their inclusion into 5 ensembles that are explored in this paper. LDA stands for linear discriminant analysis; EN for Elastic Net (Generalized Linear Model); SVM for Support Vector Machine, and RF for Random Forest. Sensitivity, specificity and area under the ROC [receiver operator characteristics] Curve (AUC) for the individual classifiers were estimated using cross-validation.

Günther et al.

Günther et al. BMC Bioinformatics 2012 13:326   doi:10.1186/1471-2105-13-326

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