Unbiased Feature Selection in Learning Random Forests for High-Dimensional Data

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