Table 3: The prediction test accuracy (mean% ± std-dev%) of the models on the image datasets against the number of trees . The number of feature dimensions in each subdataset is fixed. Numbers in bold are the best results.

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

CaltechM3000 xRF 95.50 ± .2 96.50 ± .1 96.50 ± .2 97.00 ± .1 97.50 ± .2
RF 70.00 ± .7 76.00 ± .9 77.50 ± 1.2 82.50 ± 1.6 81.50 ± .2
wsRF 91.50 ± .4 91.00 ± .3 93.00 ± .2 94.50 ± .4 92.00 ± .9
GRRF 93.00 ± .2 96.00 ± .2 94.50 ± .2 95.00 ± .3 94.00 ± .2

HorseM3000 xRF 80.59 ± .4 81.76 ± .2 79.71 ± .6 80.29 ± .1 77.65 ± .5
RF 50.59 ± 1.0 52.94 ± .8 56.18 ± .4 58.24 ± .5 57.35 ± .9
wsRF 62.06 ± .4 68.82 ± .3 67.65 ± .3 67.65 ± .5 65.88 ± .7
GRRF 65.00 ± .9 63.53 ± .3 68.53 ± .3 63.53 ± .9 71.18 ± .4

YaleB.EigenfaceM504 xRF 75.68 ± .1 85.65 ± .1 88.08 ± .1 88.94 ± .0 91.22 ± .0
RF 71.93 ± .1 79.48 ± .1 80.69 ± .1 81.67 ± .1 82.89 ± .1
wsRF 77.60 ± .1 85.61 ± .0 88.11 ± .0 89.31 ± .0 90.68 ± .0
GRRF 74.73 ± .0 84.70 ± .1 87.25 ± .0 89.61 ± .0 91.89 ± .0

YaleB.randomfaceM504 xRF 94.71 ± .0 97.64 ± .0 98.01 ± .0 98.22 ± .0 98.59 ± .0
RF 88.00 ± .0 92.59 ± .0 94.13 ± .0 94.86 ± .0 96.06 ± .0
wsRF 95.40 ± .0 97.90 ± .0 98.17 ± .0 98.14 ± .0 98.38 ± .0
GRRF 95.66 ± .0 98.10 ± .0 98.42 ± .0 98.92 ± .0 98.84 ± .0

ORL.EigenfaceM504 xRF 76.25 ± .6 87.25 ± .3 91.75 ± .2 93.25 ± .2 94.75 ± .2
RF 71.75 ± .2 78.75 ± .4 82.00 ± .3 82.75 ± .3 85.50 ± .5
wsRF 78.25 ± .4 88.75 ± .3 90.00 ± .1 91.25 ± .2 92.50 ± .2
GRRF 73.50 ± .6 85.00 ± .2 90.00 ± .1 90.75 ± .3 94.75 ± .1

ORL.randomfaceM504 xRF 87.75 ± .3 92.50 ± .2 95.50 ± .1 94.25 ± .1 96.00 ± .1
RF 77.50 ± .3 82.00 ± .7 84.50 ± .2 87.50 ± .2 86.00 ± .2
wsRF 87.00 ± .5 93.75 ± .2 93.75 ± .0 95.00 ± .1 95.50 ± .1
GRRF 87.25 ± .1 93.25 ± .1 94.50 ± .1 94.25 ± .1 95.50 ± .1