Table 4: AUC results (mean ± std-dev%) of random forest models against the number of trees on the CaltechM3000 and HorseM3000 subdatasets. The bold value in each row indicates the best result.

Dataset Model = 20 = 50 = 80 = 100 = 200

CaltechM3000 xRF .995 ± .0 .999 ± .5 1.00 ± .2 1.00 ± .1 1.00 ± .1
RF .851 ± .7 .817 ± .4 .826 ± 1.2 .865 ± .6 .864 ± 1
wsRF .841 ± 1 .845 ± .8 .834 ± .7 .850 ± .8 .870 ± .9
GRRF .846 ± .1 .860 ± .2 .862 ± .1 .908 ± .1 .923 ± .1

HorseM3000 xRF .849 ± .1 .887 ± .0 .895 ± .0 .898 ± .0 .897 ± .0
RF .637 ± .4 .664 ± .7 .692 ± 1.5 .696 ± .3 .733 ± .9
wsRF .635 ± .8 .687 ± .4 .679 ± .6 .671 ± .4 .718 ± .9
GRRF .786 ± .3 .778 ± .3 .785 ± .8 .699 ± .1 .806 ± .4