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

Table 2

The () error bound results of random forest models against the number of codebook size on the Caltech and Horse datasets. The bold value in each row indicates the best result.

Dataset	Model	300	500	1000	3000	5000	7000	10000	12000	15000
Caltech	xRF	.0312	.0271	.0280	.0287	.0357	.0440	.0650	.0742	.0789
	RF	.0369	.0288	.0294	.0327	.0435	.0592	.0908	.1114	.3611
	wsRF	.0413	.0297	.0268	.0221	.0265	.0333	.0461	.0456	.0789
Horse	xRF	.0266	.0262	.0246	.0277	.0259	.0298	.0275	.0288	.0382
	RF	.0331	.0342	.0354	.0374	.0417	.0463	.0519	.0537	.0695
	wsRF	.0429	.0414	.0391	.0295	.0288	.0333	.0295	.0339	.0455