Table 5.

Test error of customized training and the five other methods described in Section 2.5.1 on 16 benchmark data sets. The bold text indicates the best performance for each data set. Customized training is competitive with the other methods and improves on standard training more often than not

			ST	CT		SVM	KSVM	RF	KNN
Data	n	p	Error	Error	G	Error	Error	Error	Error	k	%Imp*
BS	313	4	0.112	0.099	3	0.086	0.131	0.131	0.105	20	11.4
BCW	285	30	0.028	0.035	2	0.035	0.038	0.028	0.056	63	–25
C	1598	38	0.026	0.021	10	0.029	0.046	0.006	0.085	36	18.5
CMC	737	18	0.485	0.440	5	0.479	0.523	0.472	0.523	32	9.2
F	50	9	0.160	0.160	1	0.160	0.160	0.180	0.180	2	–
FTP	3059	51	0.557	0.530	5	0.489	0.444	0.427	0.508	47	4.7
LSVT	63	310	0.126	0.142	1	0.111	0.365	0.095	0.222	15	–12.5
M	4062	96	0.000	0.000	1	0.001	0.001	0.000	0.001	15	–
ORHD	3823	62	0.046	0.043	2	0.032	0.049	0.027	0.055	38	6.0
P	98	22	0.268	0.144	3	0.154	0.144	0.082	0.123	5	46.1
Q	528	41	0.176	0.134	5	0.146	0.148	0.140	0.144	19	23.6
S	105	7	0.047	0.047	2	0.066	0.114	0.104	0.066	9	–
SPF	971	27	0.321	0.278	5	0.273	0.281	0.246	0.357	57	13.3
TAE	76	53	0.720	0.470	10	0.653	0.613	0.506	0.493	1	34.6
UKM	258	5	0.041	0.013	1	0.103	0.213	0.068	0.565	79	66.6
V	528	10	0.610	0.491	2	0.387	0.480	0.409	0.508	1	19.5

^*%Imp: Percent relative improvement of customized training to standard training.