Table 6

Summary of classification performance for the Vote Threshold aggregation method
VOTE THRESHOLD Sensitivity Specificity AUC
Ensemble classifier Individual classifiers Ensemble classifier Individual classifiers Ensemble classifier Individual classifiers
min max average min max average min max average
Ensemble A 1.0000 0.9211 0.9737 0.9474 0.7368 0.8421 0.8421 0.8421 0.9875 0.9626 0.9737 0.9681
Ensemble B 1.0000 0.9211 0.9211 0.9211 0.6842 0.8421 0.8421 0.8421 0.9917 0.9557 0.9612 0.9584
Ensemble C 1.0000 0.9211 0.9737 0.9539 0.6842 0.7368 0.8947 0.8421 0.9861 0.9501 0.9709 0.9602
Ensemble D 1.0000 0.9211 0.9737 0.9386 0.7368 0.8421 0.9474 0.9035 0.9875 0.9557 0.9778 0.9677
Ensemble E 1.0000 0.8947 1.0000 0.9518 0.6316 0.7368 0.9474 0.8553 0.9903 0.9418 0.9765 0.9643
Ensemble F 1.0000 0.9211 0.9737 0.9430 0.6842 0.8421 0.9474 0.8816 0.9931 0.9557 0.9848 0.9672

Shown is performance for tumour vs normal classification for the 6 ensembles defined in Table 4 using the vote threshold aggregation method. Similarly to Table 5, individual classifier performances are included for comparison.

Günther et al.

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

Open Data