Table 3.

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

Dataset	Model	K = 20	K = 50	K = 80	K = 100	K = 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